Athero-Express Biobank Study – MCP1 plaque levels (n ± 1,100; [pg/mL]).
An ‘Athero-Express Biobank Study’ project
Sander W. van der Laan, PhD | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans
2020-07-14
Preparation
Clean the environment.
Set locations, and the working directory.
A package-installation function.
Load those packages.
We will create a datestamp and define the Utrecht Science Park Colour Scheme.
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
tvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
R = summary(fit)$r.squared
R.adj = summary(fit)$adj.r.squared
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("T-value...................:", round(tvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("R^2.......................:", round(R, 6), "\n")
cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}
GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,9))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data...\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
zvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
dev <- fit$deviance
nullDev <- fit$null.deviance
modelN <- length(fit$fitted.values)
R.l <- 1 - dev / nullDev
R.cs <- 1 - exp(-(nullDev - dev) / modelN)
R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("Z-value...................:", round(zvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}Background
Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.
Objectives
Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.
Methods
We used the Luminex-platform to measure atherosclerotic plaque proteins. Historically, this was done in two experiments:
Experiment 1:
This entails an experiment where also 20+ other interleukins, cyto- and chemokines, and metalloproteinases were measured. Part of these were measured using LUMINEX, some of them were measured using FACS, ELISA, and activity assays. These assays were run according to instructions from the producer in a research setting.
- variable
MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/mL]. Luminex platform.
Experiment 2:
This entails an experiment where MCP1 was measured in a clinical diagnostic settings on a clinically validated Luminex-platform. - variable MCP1_pg_ml_2015: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/mL]. Luminex platform.
Loading data
Clinical data
Loading Athero-Express clinical data.
require(haven)
# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDBraw <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
head(AEDBraw)Plaque protein data
Loading Athero-Express plaque protein measurements from 2015.
library(openxlsx)
AEDB_Protein_2015 <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_Proteins/Cytokines_and_chemokines_2015/20200629_MPCF015-0024.xlsx"), sheet = "for_SPSS_R")
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "SampleID"] <- "STUDY_NUMBER"
head(AEDB_Protein_2015)NAMerging protein data
We will merge these measurements to the AEDB (pg/mL measurements of MCP1). We also gathered more information on the experiment.
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6_pg_ml"] <- "IL6_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6R_pg_ml"] <- "IL6R_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL8_pg_ml"] <- "IL8_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCP1_pg_ml"] <- "MCP1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "RANTES_pg_ml"] <- "RANTES_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "PAI1_pg_ml"] <- "PAI1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCSF_pg_ml"] <- "MCSF_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Adiponectin_ng_ml"] <- "Adiponectin_ng_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Segment_isolated_Tris"] <- "Segment_isolated_Tris_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Tris_protein_conc_ug_ml"] <- "Tris_protein_conc_ug_ml_2015"
temp <- subset(AEDB_Protein_2015, select = c("STUDY_NUMBER", "IL6_pg_ml_2015", "IL6R_pg_ml_2015", "IL8_pg_ml_2015", "MCP1_pg_ml_2015", "RANTES_pg_ml_2015", "PAI1_pg_ml_2015", "MCSF_pg_ml_2015", "Adiponectin_ng_ml_2015", "Segment_isolated_Tris_2015", "Tris_protein_conc_ug_ml_2015"))
AEDB <- merge(AEDBraw, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
all.x = TRUE)
rm(temp)
temp <- subset(AEDB, select = c("STUDY_NUMBER", "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015", "Segment_isolated_Tris_2015"))
dim(temp)[1] 3793 5head(temp)rm(temp) Examine AEDB
We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is.
There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/.
AEDB %>% sjPlot::view_df(show.type = TRUE,
show.frq = TRUE,
show.prc = TRUE,
show.na = TRUE,
max.len = TRUE,
wrap.labels = 20,
verbose = FALSE,
use.viewer = FALSE,
file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) Following 3 variables have only missing values and are not shown:yearpsy5 [326], yearchol3 [347], yearablo3 [419]Fixing and creating variables
We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.
Coding of symptoms is as follows:
- missing -999
- Asymptomatic 0
- TIA 1
- minor stroke 2
- Major stroke 3
- Amaurosis fugax 4
- Four vessel disease 5
- Vertebrobasilary TIA 7
- Retinal infarction 8
- Symptomatic, but aspecific symtoms 9
- Contralateral symptomatic occlusion 10
- retinal infarction 11
- armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
- retinal infarction + TIAs 13
- Ocular ischemic syndrome 14
- ischemisch glaucoom 15
- subclavian steal syndrome 16
- TGA 17
We will group as follows in Symptoms.5G:
- Asymptomatic > 0
- TIA > 1, 7, 13
- Stroke > 2, 3
- Ocular > 4, 14, 15
- Retinal infarction > 8, 11
- Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3
- Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt2G:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms
attach(AEDB)
AEDB$sympt[is.na(AEDB$sympt)] <- -999
# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"
# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"
detach(AEDB)
# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
#
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
Asymptomatic Ocular Other Retinal infarction Stroke TIA <NA>
Asymptomatic 333 0 0 0 0 0 0
Symptomatic 0 417 119 43 733 1045 0
<NA> 0 0 0 0 0 0 1103# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
#
# rm(AEDB.temp)
We will also fix the plaquephenotypes variable.
Coding of symptoms is as follows:
- missing -999
- not relevant -888
- fibrous 1
- fibroatheromatous 2
- atheromatous 3
# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)
table(AEDB$OverallPlaquePhenotype)
atheromatous fibroatheromatous fibrous
550 843 1439 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the diabetes status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)
table(AEDB$DM.composite)
0 1
2766 985 table(AEDB$DiabetesStatus)
Control (no Diabetes Dx/Med) Diabetes
2766 985 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.
diet801: are you a smoker?diet802: did you smoke in the past?
We already have some variables indicating smoking status:
SmokingReported: patient has reported to smoke.SmokingYearOR: smoking in the year of surgery?SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)
# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
#
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))
cat("\nFixing smoking status.\n")
Fixing smoking status.attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)
cat("\n* Current smoking status.\n")
* Current smoking status.table(AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Current smoker"))Current smoker
no data available/missing no yes <NA>
0 2364 1310 119 cat("\n* Updated smoking status.\n")
* Updated smoking status.table(AEDB$SmokerStatus,
useNA = "ifany",
dnn = c("Updated smoking status"))Updated smoking status
Current smoker Ex-smoker Never smoked <NA>
1310 1814 389 280 cat("\n* Comparing to 'SmokerCurrent'.\n")
* Comparing to 'SmokerCurrent'.table(AEDB$SmokerStatus, AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Updated smoking status", "Current smoker")) Current smoker
Updated smoking status no data available/missing no yes <NA>
Current smoker 0 0 1310 0
Ex-smoker 0 1814 0 0
Never smoked 0 389 0 0
<NA> 0 161 0 119# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the alcohol status variable.
# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)
table(AEDB$AlcoholUse)
No Yes
1238 2346 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv
# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)
table(AEDB$CAD_history)
0 1
2432 1285 table(AEDB$Stroke_history)
0 1
2764 948 table(AEDB$Peripheral.interv)
0 1
2581 1099 table(AEDB$MedHx_CVD)
No yes
1310 2476 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
Athero-Express Biobank Study
Baseline characteristics
We are interested in the following variables at baseline.
- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
- Never smokers
- Ex-smokers
- Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
- Asymptomatic
- Amaurosis fugax
- TIA
- Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments
MCP1, andMCP1_pg_ml_2015)
cat("===========================================================================================\n")===========================================================================================cat("CREATE BASELINE TABLE\n")CREATE BASELINE TABLE# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
"Age", "Gender",
"TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time",
"macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
"neutrophils", "Mast_cells_plaque",
"IPH.bin", "vessel_density_averaged",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
"IL6", "IL6R_pg_ml_2015",
"MCP1", "MCP1_pg_ml_2015")
basetable_bin = c("Gender",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conAll patients
Showing the baseline table of the whole Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 3793
Hospital % (freq) St. Antonius, Nieuwegein 45.7 (1735) 0.0
UMC Utrecht 54.3 (2058)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 2.5 ( 94)
2003 5.4 ( 204)
2004 7.6 ( 289)
2005 8.1 ( 309)
2006 7.5 ( 285)
2007 6.2 ( 234)
2008 5.9 ( 223)
2009 7.1 ( 268)
2010 8.1 ( 307)
2011 7.1 ( 270)
2012 8.2 ( 312)
2013 6.9 ( 262)
2014 7.9 ( 299)
2015 2.1 ( 79)
2016 3.3 ( 124)
2017 2.2 ( 85)
2018 2.1 ( 80)
2019 1.8 ( 69)
Age (mean (SD)) 68.906 (9.322) 0.0
Gender % (freq) female 30.6 (1161) 0.0
male 69.4 (2632)
TC_finalCU (mean (SD)) 185.256 (81.509) 46.8
LDL_finalCU (mean (SD)) 106.533 (40.725) 54.5
HDL_finalCU (mean (SD)) 46.591 (16.725) 51.1
TG_finalCU (mean (SD)) 154.212 (99.774) 51.8
TC_final (mean (SD)) 4.798 (2.111) 46.8
LDL_final (mean (SD)) 2.759 (1.055) 54.5
HDL_final (mean (SD)) 1.207 (0.433) 51.1
TG_final (mean (SD)) 1.743 (1.127) 51.8
hsCRP_plasma (mean (SD)) 19.231 (206.750) 60.6
systolic (mean (SD)) 150.901 (25.114) 13.5
diastoli (mean (SD)) 79.933 (21.847) 13.5
GFR_MDRD (mean (SD)) 74.844 (24.740) 6.5
BMI (mean (SD)) 26.336 (4.050) 7.5
KDOQI % (freq) No data available/missing 0.0 ( 0) 6.6
Normal kidney function 22.1 ( 839)
CKD 2 (Mild) 47.2 (1789)
CKD 3 (Moderate) 21.9 ( 831)
CKD 4 (Severe) 1.4 ( 53)
CKD 5 (Failure) 0.8 ( 32)
<NA> 6.6 ( 249)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 7.5
Underweight 1.2 ( 44)
Normal 35.2 (1336)
Overweight 42.1 (1595)
Obese 14.1 ( 533)
<NA> 7.5 ( 285)
SmokerStatus % (freq) Current smoker 34.5 (1310) 7.4
Ex-smoker 47.8 (1814)
Never smoked 10.3 ( 389)
<NA> 7.4 ( 280)
AlcoholUse % (freq) No 32.6 (1238) 5.5
Yes 61.9 (2346)
<NA> 5.5 ( 209)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 72.9 (2766) 1.1
Diabetes 26.0 ( 985)
<NA> 1.1 ( 42)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 4.0
no 23.7 ( 900)
yes 72.3 (2742)
<NA> 4.0 ( 151)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 5.5
no 28.6 (1086)
yes 65.9 (2500)
<NA> 5.5 ( 207)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.3
no 13.3 ( 505)
yes 85.4 (3240)
<NA> 1.3 ( 48)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.0 ( 798)
yes 77.5 (2940)
<NA> 1.5 ( 55)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 85.6 (3248)
yes 12.8 ( 485)
<NA> 1.6 ( 60)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.6
no 13.7 ( 521)
yes 84.7 (3213)
<NA> 1.6 ( 59)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.8 ( 826)
yes 76.7 (2911)
<NA> 1.5 ( 56)
Stroke_Dx % (freq) Missing 0.0 ( 0) 8.1
No stroke diagnosed 74.4 (2823)
Stroke diagnosed 17.5 ( 663)
<NA> 8.1 ( 307)
sympt % (freq) missing 29.1 (1103) 0.0
Asymptomatic 8.8 ( 333)
TIA 27.4 (1040)
minor stroke 12.1 ( 458)
Major stroke 7.3 ( 275)
Amaurosis fugax 10.5 ( 399)
Four vessel disease 1.1 ( 43)
Vertebrobasilary TIA 0.1 ( 5)
Retinal infarction 1.0 ( 37)
Symptomatic, but aspecific symtoms 1.6 ( 61)
Contralateral symptomatic occlusion 0.3 ( 12)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.5 ( 18)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular 11.0 ( 417)
Other 3.1 ( 119)
Retinal infarction 1.1 ( 43)
Stroke 19.3 ( 733)
TIA 27.6 (1045)
<NA> 29.1 (1103)
AsymptSympt % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular and others 15.3 ( 579)
Symptomatic 46.9 (1778)
<NA> 29.1 (1103)
AsymptSympt2G % (freq) Asymptomatic 8.8 ( 333) 29.1
Symptomatic 62.1 (2357)
<NA> 29.1 (1103)
restenos % (freq) missing 0.0 ( 0) 4.0
de novo 87.0 (3299)
restenosis 8.8 ( 334)
stenose bij angioseal na PTCA 0.2 ( 7)
<NA> 4.0 ( 153)
stenose % (freq) missing 0.0 ( 0) 7.0
0-49% 0.7 ( 25)
50-70% 6.8 ( 257)
70-90% 35.6 (1349)
90-99% 29.9 (1133)
100% (Occlusion) 14.8 ( 560)
NA 0.1 ( 3)
50-99% 2.6 ( 99)
70-99% 2.6 ( 100)
99 0.1 ( 2)
<NA> 7.0 ( 265)
MedHx_CVD % (freq) No 34.5 (1310) 0.2
yes 65.3 (2476)
<NA> 0.2 ( 7)
CAD_history % (freq) Missing 0.0 ( 0) 2.0
No history CAD 64.1 (2432)
History CAD 33.9 (1285)
<NA> 2.0 ( 76)
PAOD % (freq) missing/no data 0.0 ( 0) 1.6
no 55.1 (2090)
yes 43.3 (1644)
<NA> 1.6 ( 59)
Peripheral.interv % (freq) no 68.0 (2581) 3.0
yes 29.0 (1099)
<NA> 3.0 ( 113)
EP_composite % (freq) No data available. 0.0 ( 0) 7.3
No composite endpoints 60.6 (2299)
Composite endpoints 32.1 (1218)
<NA> 7.3 ( 276)
EP_composite_time (mean (SD)) 2.267 (1.203) 7.4
macmean0 (mean (SD)) 0.656 (1.154) 32.4
smcmean0 (mean (SD)) 2.292 (6.618) 32.4
Macrophages.bin % (freq) no/minor 42.3 (1603) 25.7
moderate/heavy 32.1 (1216)
<NA> 25.7 ( 974)
SMC.bin % (freq) no/minor 22.9 ( 870) 25.3
moderate/heavy 51.8 (1964)
<NA> 25.3 ( 959)
neutrophils (mean (SD)) 162.985 (490.469) 91.0
Mast_cells_plaque (mean (SD)) 165.663 (163.421) 93.0
IPH.bin % (freq) no 32.3 (1225) 24.8
yes 42.9 (1628)
<NA> 24.8 ( 940)
vessel_density_averaged (mean (SD)) 8.030 (6.344) 48.0
Calc.bin % (freq) no/minor 37.9 (1438) 24.7
moderate/heavy 37.4 (1417)
<NA> 24.7 ( 938)
Collagen.bin % (freq) no/minor 14.2 ( 540) 25.2
moderate/heavy 60.6 (2299)
<NA> 25.2 ( 954)
Fat.bin_10 % (freq) <10% 32.3 (1226) 24.7
>10% 43.0 (1630)
<NA> 24.7 ( 937)
Fat.bin_40 % (freq) <40% 60.0 (2276) 24.7
>40% 15.3 ( 580)
<NA> 24.7 ( 937)
OverallPlaquePhenotype % (freq) atheromatous 14.5 ( 550) 25.3
fibroatheromatous 22.2 ( 843)
fibrous 37.9 (1439)
<NA> 25.3 ( 961)
IL6 (mean (SD)) 94.451 (278.490) 84.5
IL6R_pg_ml_2015 (mean (SD)) 219.949 (252.513) 67.0
MCP1 (mean (SD)) 130.926 (118.422) 83.7
MCP1_pg_ml_2015 (mean (SD)) 587.541 (843.110) 65.3 CEA patients
Showing the baseline table of the CEA patients in the Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB.CEA, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 2423
Hospital % (freq) St. Antonius, Nieuwegein 39.1 ( 948) 0.0
UMC Utrecht 60.9 (1475)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 3.3 ( 81)
2003 6.5 ( 157)
2004 7.8 ( 190)
2005 7.6 ( 185)
2006 7.6 ( 183)
2007 6.3 ( 152)
2008 5.7 ( 138)
2009 7.5 ( 182)
2010 6.6 ( 159)
2011 6.8 ( 164)
2012 7.3 ( 176)
2013 6.1 ( 149)
2014 6.7 ( 163)
2015 3.1 ( 76)
2016 3.5 ( 85)
2017 2.7 ( 65)
2018 2.7 ( 66)
2019 2.1 ( 52)
Age (mean (SD)) 69.103 (9.302) 0.0
Gender % (freq) female 30.5 ( 739) 0.0
male 69.5 (1684)
TC_finalCU (mean (SD)) 184.852 (56.275) 38.0
LDL_finalCU (mean (SD)) 108.484 (41.794) 45.6
HDL_finalCU (mean (SD)) 46.432 (16.999) 41.7
TG_finalCU (mean (SD)) 151.189 (91.249) 42.8
TC_final (mean (SD)) 4.788 (1.458) 38.0
LDL_final (mean (SD)) 2.810 (1.082) 45.6
HDL_final (mean (SD)) 1.203 (0.440) 41.7
TG_final (mean (SD)) 1.708 (1.031) 42.8
hsCRP_plasma (mean (SD)) 19.887 (231.453) 52.9
systolic (mean (SD)) 152.408 (25.163) 11.3
diastoli (mean (SD)) 81.314 (25.178) 11.3
GFR_MDRD (mean (SD)) 73.115 (21.145) 5.4
BMI (mean (SD)) 26.488 (3.976) 5.9
KDOQI % (freq) No data available/missing 0.0 ( 0) 5.4
Normal kidney function 19.1 ( 462)
CKD 2 (Mild) 50.9 (1233)
CKD 3 (Moderate) 22.9 ( 554)
CKD 4 (Severe) 1.3 ( 32)
CKD 5 (Failure) 0.4 ( 10)
<NA> 5.4 ( 132)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 5.9
Underweight 1.0 ( 24)
Normal 35.1 ( 851)
Overweight 43.4 (1052)
Obese 14.5 ( 352)
<NA> 5.9 ( 144)
SmokerStatus % (freq) Current smoker 33.2 ( 805) 5.9
Ex-smoker 48.0 (1163)
Never smoked 12.9 ( 313)
<NA> 5.9 ( 142)
AlcoholUse % (freq) No 34.5 ( 835) 4.1
Yes 61.5 (1489)
<NA> 4.1 ( 99)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 75.2 (1822) 1.1
Diabetes 23.7 ( 574)
<NA> 1.1 ( 27)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 3.2
no 24.3 ( 590)
yes 72.4 (1755)
<NA> 3.2 ( 78)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 4.4
no 30.0 ( 726)
yes 65.6 (1590)
<NA> 4.4 ( 107)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.2
no 14.6 ( 354)
yes 84.2 (2041)
<NA> 1.2 ( 28)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.4
no 23.4 ( 566)
yes 75.3 (1824)
<NA> 1.4 ( 33)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 87.3 (2116)
yes 11.1 ( 269)
<NA> 1.6 ( 38)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.5
no 12.2 ( 295)
yes 86.3 (2092)
<NA> 1.5 ( 36)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.4
no 20.3 ( 491)
yes 78.3 (1898)
<NA> 1.4 ( 34)
Stroke_Dx % (freq) Missing 0.0 ( 0) 6.9
No stroke diagnosed 71.5 (1732)
Stroke diagnosed 21.7 ( 525)
<NA> 6.9 ( 166)
sympt % (freq) missing 0.0 ( 0) 0.0
Asymptomatic 11.1 ( 270)
TIA 39.7 ( 961)
minor stroke 16.8 ( 407)
Major stroke 9.9 ( 239)
Amaurosis fugax 15.7 ( 380)
Four vessel disease 1.6 ( 38)
Vertebrobasilary TIA 0.2 ( 5)
Retinal infarction 1.4 ( 34)
Symptomatic, but aspecific symtoms 2.2 ( 53)
Contralateral symptomatic occlusion 0.5 ( 11)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.7 ( 16)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 11.1 ( 270) 0.0
Ocular 16.3 ( 396)
Other 4.3 ( 105)
Retinal infarction 1.7 ( 40)
Stroke 26.7 ( 646)
TIA 39.9 ( 966)
AsymptSympt % (freq) Asymptomatic 11.1 ( 270) 0.0
Ocular and others 22.3 ( 541)
Symptomatic 66.5 (1612)
AsymptSympt2G % (freq) Asymptomatic 11.1 ( 270) 0.0
Symptomatic 88.9 (2153)
restenos % (freq) missing 0.0 ( 0) 1.4
de novo 93.7 (2270)
restenosis 4.9 ( 118)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 1.4 ( 35)
stenose % (freq) missing 0.0 ( 0) 2.0
0-49% 0.5 ( 13)
50-70% 7.8 ( 190)
70-90% 46.5 (1127)
90-99% 38.3 ( 928)
100% (Occlusion) 1.3 ( 31)
NA 0.0 ( 1)
50-99% 0.6 ( 15)
70-99% 2.8 ( 68)
99 0.1 ( 2)
<NA> 2.0 ( 48)
MedHx_CVD % (freq) No 36.9 ( 893) 0.0
yes 63.1 (1530)
CAD_history % (freq) Missing 0.0 ( 0) 1.9
No history CAD 66.9 (1620)
History CAD 31.2 ( 756)
<NA> 1.9 ( 47)
PAOD % (freq) missing/no data 0.0 ( 0) 2.0
no 77.5 (1878)
yes 20.5 ( 497)
<NA> 2.0 ( 48)
Peripheral.interv % (freq) no 77.2 (1870) 2.9
yes 19.9 ( 482)
<NA> 2.9 ( 71)
EP_composite % (freq) No data available. 0.0 ( 0) 5.0
No composite endpoints 70.6 (1711)
Composite endpoints 24.3 ( 590)
<NA> 5.0 ( 122)
EP_composite_time (mean (SD)) 2.479 (1.109) 5.2
macmean0 (mean (SD)) 0.767 (1.183) 29.7
smcmean0 (mean (SD)) 1.985 (2.380) 29.9
Macrophages.bin % (freq) no/minor 35.0 ( 847) 24.1
moderate/heavy 40.9 ( 992)
<NA> 24.1 ( 584)
SMC.bin % (freq) no/minor 24.8 ( 602) 23.8
moderate/heavy 51.3 (1244)
<NA> 23.8 ( 577)
neutrophils (mean (SD)) 147.151 (419.998) 87.5
Mast_cells_plaque (mean (SD)) 164.488 (163.771) 90.0
IPH.bin % (freq) no 30.8 ( 746) 23.5
yes 45.7 (1108)
<NA> 23.5 ( 569)
vessel_density_averaged (mean (SD)) 8.317 (6.384) 35.1
Calc.bin % (freq) no/minor 41.6 (1007) 23.4
moderate/heavy 35.1 ( 850)
<NA> 23.4 ( 566)
Collagen.bin % (freq) no/minor 15.8 ( 382) 23.6
moderate/heavy 60.6 (1469)
<NA> 23.6 ( 572)
Fat.bin_10 % (freq) <10% 22.4 ( 542) 23.3
>10% 54.3 (1316)
<NA> 23.3 ( 565)
Fat.bin_40 % (freq) <40% 56.2 (1362) 23.3
>40% 20.5 ( 496)
<NA> 23.3 ( 565)
OverallPlaquePhenotype % (freq) atheromatous 19.8 ( 480) 23.7
fibroatheromatous 27.8 ( 674)
fibrous 28.7 ( 695)
<NA> 23.7 ( 574)
IL6 (mean (SD)) 98.812 (292.457) 78.2
IL6R_pg_ml_2015 (mean (SD)) 217.355 (248.551) 52.4
MCP1 (mean (SD)) 135.763 (120.028) 76.7
MCP1_pg_ml_2015 (mean (SD)) 600.444 (858.416) 50.5 CEA patients with MCP1_pg_ml_2015
Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ml_2015.
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ml_2015))
AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test
n 131 1068
Hospital % (freq) St. Antonius, Nieuwegein 50.4 ( 66) 46.4 ( 496) 0.447
UMC Utrecht 49.6 ( 65) 53.6 ( 572)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
2002 10.7 ( 14) 3.9 ( 42)
2003 7.6 ( 10) 9.4 ( 100)
2004 17.6 ( 23) 11.5 ( 123)
2005 9.9 ( 13) 11.1 ( 119)
2006 10.7 ( 14) 10.2 ( 109)
2007 11.5 ( 15) 10.5 ( 112)
2008 7.6 ( 10) 7.4 ( 79)
2009 7.6 ( 10) 8.4 ( 90)
2010 5.3 ( 7) 7.6 ( 81)
2011 6.1 ( 8) 9.6 ( 102)
2012 5.3 ( 7) 8.3 ( 89)
2013 0.0 ( 0) 2.0 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 66.237 (9.184) 68.940 (9.115) 0.001
Gender % (freq) female 23.7 ( 31) 31.4 ( 335) 0.088
male 76.3 (100) 68.6 ( 733)
TC_finalCU (mean (SD)) 175.987 (47.184) 183.526 (48.426) 0.174
LDL_finalCU (mean (SD)) 102.781 (38.324) 109.377 (41.109) 0.183
HDL_finalCU (mean (SD)) 43.701 (14.754) 45.809 (18.513) 0.318
TG_finalCU (mean (SD)) 157.650 (89.246) 145.194 (84.818) 0.209
TC_final (mean (SD)) 4.558 (1.222) 4.753 (1.254) 0.174
LDL_final (mean (SD)) 2.662 (0.993) 2.833 (1.065) 0.183
HDL_final (mean (SD)) 1.132 (0.382) 1.186 (0.479) 0.318
TG_final (mean (SD)) 1.781 (1.008) 1.641 (0.958) 0.209
hsCRP_plasma (mean (SD)) 5.688 (19.440) 16.551 (113.708) 0.380
systolic (mean (SD)) 153.577 (24.327) 155.790 (26.176) 0.397
diastoli (mean (SD)) 80.622 (13.225) 82.883 (13.573) 0.097
GFR_MDRD (mean (SD)) 71.026 (20.424) 71.866 (20.055) 0.658
BMI (mean (SD)) 26.623 (3.391) 26.323 (3.744) 0.386
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
Normal kidney function 17.6 ( 23) 17.2 ( 184)
CKD 2 (Mild) 49.6 ( 65) 53.2 ( 568)
CKD 3 (Moderate) 28.2 ( 37) 24.3 ( 260)
CKD 4 (Severe) 0.0 ( 0) 1.2 ( 13)
CKD 5 (Failure) 0.8 ( 1) 0.4 ( 4)
<NA> 3.8 ( 5) 3.7 ( 39)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
Underweight 0.8 ( 1) 0.9 ( 10)
Normal 32.8 ( 43) 35.6 ( 380)
Overweight 51.1 ( 67) 46.2 ( 493)
Obese 13.0 ( 17) 12.7 ( 136)
<NA> 2.3 ( 3) 4.6 ( 49)
SmokerStatus % (freq) Current smoker 30.5 ( 40) 36.2 ( 387) 0.077
Ex-smoker 57.3 ( 75) 45.6 ( 487)
Never smoked 9.9 ( 13) 14.2 ( 152)
<NA> 2.3 ( 3) 3.9 ( 42)
AlcoholUse % (freq) No 38.2 ( 50) 33.3 ( 356) 0.347
Yes 59.5 ( 78) 62.4 ( 666)
<NA> 2.3 ( 3) 4.3 ( 46)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 76.3 (100) 77.3 ( 826) 0.882
Diabetes 23.7 ( 31) 22.7 ( 242)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 23.7 ( 31) 26.7 ( 285)
yes 75.6 ( 99) 71.2 ( 760)
<NA> 0.8 ( 1) 2.2 ( 23)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 30.5 ( 40) 33.0 ( 352)
yes 67.9 ( 89) 64.2 ( 686)
<NA> 1.5 ( 2) 2.8 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 9.9 ( 13) 14.3 ( 153)
yes 90.1 (118) 85.7 ( 915)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 14.5 ( 19) 23.3 ( 249)
yes 85.5 (112) 76.5 ( 817)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 89.3 (117) 87.9 ( 939)
yes 10.7 ( 14) 11.9 ( 127)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 6.1 ( 8) 11.0 ( 118)
yes 93.1 (122) 88.6 ( 946)
<NA> 0.8 ( 1) 0.4 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 15.3 ( 20) 22.7 ( 242)
yes 84.7 (111) 77.2 ( 824)
<NA> 0.0 ( 0) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN
No stroke diagnosed 80.2 (105) 75.2 ( 803)
Stroke diagnosed 14.5 ( 19) 19.5 ( 208)
<NA> 5.3 ( 7) 5.3 ( 57)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN
Asymptomatic 100.0 (131) 0.0 ( 0)
TIA 0.0 ( 0) 46.3 ( 494)
minor stroke 0.0 ( 0) 16.7 ( 178)
Major stroke 0.0 ( 0) 12.3 ( 131)
Amaurosis fugax 0.0 ( 0) 17.2 ( 184)
Four vessel disease 0.0 ( 0) 2.2 ( 23)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 15)
Symptomatic, but aspecific symtoms 0.0 ( 0) 2.7 ( 29)
Contralateral symptomatic occlusion 0.0 ( 0) 0.7 ( 7)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001
Ocular 0.0 ( 0) 17.3 ( 185)
Other 0.0 ( 0) 5.6 ( 60)
Retinal infarction 0.0 ( 0) 1.7 ( 18)
Stroke 0.0 ( 0) 28.9 ( 309)
TIA 0.0 ( 0) 46.4 ( 496)
AsymptSympt % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001
Ocular and others 0.0 ( 0) 24.6 ( 263)
Symptomatic 0.0 ( 0) 75.4 ( 805)
AsymptSympt2G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001
Symptomatic 0.0 ( 0) 100.0 (1068)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN
de novo 93.9 (123) 94.8 (1012)
restenosis 2.3 ( 3) 3.2 ( 34)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 2.1 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN
0-49% 0.0 ( 0) 0.6 ( 6)
50-70% 3.1 ( 4) 6.5 ( 69)
70-90% 51.1 ( 67) 44.5 ( 475)
90-99% 41.2 ( 54) 42.7 ( 456)
100% (Occlusion) 0.0 ( 0) 0.9 ( 10)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.8 ( 1) 0.5 ( 5)
70-99% 0.0 ( 0) 1.3 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 3.1 ( 33)
MedHx_CVD % (freq) No 38.9 ( 51) 36.9 ( 394) 0.719
yes 61.1 ( 80) 63.1 ( 674)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN
No history CAD 61.8 ( 81) 69.9 ( 746)
History CAD 38.2 ( 50) 30.1 ( 322)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN
no 74.0 ( 97) 79.6 ( 850)
yes 26.0 ( 34) 20.4 ( 218)
Peripheral.interv % (freq) no 74.0 ( 97) 82.5 ( 881) 0.043
yes 26.0 ( 34) 17.2 ( 184)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN
No composite endpoints 67.2 ( 88) 74.3 ( 793)
Composite endpoints 32.8 ( 43) 24.9 ( 266)
<NA> 0.0 ( 0) 0.8 ( 9)
EP_composite_time (mean (SD)) 2.614 (0.931) 2.613 (1.094) 0.992
macmean0 (mean (SD)) 0.837 (1.088) 0.780 (1.229) 0.616
smcmean0 (mean (SD)) 2.152 (1.861) 1.904 (2.220) 0.223
Macrophages.bin % (freq) no/minor 48.9 ( 64) 47.5 ( 507) 0.586
moderate/heavy 50.4 ( 66) 50.5 ( 539)
<NA> 0.8 ( 1) 2.1 ( 22)
SMC.bin % (freq) no/minor 22.9 ( 30) 32.1 ( 343) 0.088
moderate/heavy 75.6 ( 99) 66.0 ( 705)
<NA> 1.5 ( 2) 1.9 ( 20)
neutrophils (mean (SD)) 157.643 (507.380) 172.872 (477.038) 0.876
Mast_cells_plaque (mean (SD)) 111.400 (112.037) 183.284 (180.156) 0.056
IPH.bin % (freq) no 41.2 ( 54) 38.1 ( 407) 0.577
yes 58.0 ( 76) 60.1 ( 642)
<NA> 0.8 ( 1) 1.8 ( 19)
vessel_density_averaged (mean (SD)) 8.608 (6.547) 8.403 (6.461) 0.744
Stratified by AsymptSympt2G
Missing
n
Hospital % (freq) 0.0
ORyear % (freq) 0.0
Age (mean (SD)) 0.0
Gender % (freq) 0.0
TC_finalCU (mean (SD)) 33.5
LDL_finalCU (mean (SD)) 39.7
HDL_finalCU (mean (SD)) 36.4
TG_finalCU (mean (SD)) 36.1
TC_final (mean (SD)) 33.5
LDL_final (mean (SD)) 39.7
HDL_final (mean (SD)) 36.4
TG_final (mean (SD)) 36.1
hsCRP_plasma (mean (SD)) 38.8
systolic (mean (SD)) 14.0
diastoli (mean (SD)) 14.0
GFR_MDRD (mean (SD)) 3.6
BMI (mean (SD)) 4.2
KDOQI % (freq) 3.7
BMI_WHO % (freq) 4.3
SmokerStatus % (freq) 3.8
AlcoholUse % (freq) 4.1
DiabetesStatus % (freq) 0.0
Hypertension.selfreport % (freq) 2.0
Hypertension.selfreportdrug % (freq) 2.7
Hypertension.composite % (freq) 0.0
Hypertension.drugs % (freq) 0.2
Med.anticoagulants % (freq) 0.2
Med.all.antiplatelet % (freq) 0.4
Med.Statin.LLD % (freq) 0.2
Stroke_Dx % (freq) 5.3
sympt % (freq) 0.0
Symptoms.5G % (freq) 0.0
AsymptSympt % (freq) 0.0
AsymptSympt2G % (freq) 0.0
restenos % (freq) 2.3
stenose % (freq) 3.2
MedHx_CVD % (freq) 0.0
CAD_history % (freq) 0.0
PAOD % (freq) 0.0
Peripheral.interv % (freq) 0.3
EP_composite % (freq) 0.8
EP_composite_time (mean (SD)) 0.9
macmean0 (mean (SD)) 2.3
smcmean0 (mean (SD)) 2.7
Macrophages.bin % (freq) 1.9
SMC.bin % (freq) 1.8
neutrophils (mean (SD)) 82.0
Mast_cells_plaque (mean (SD)) 86.2
IPH.bin % (freq) 1.7
vessel_density_averaged (mean (SD)) 8.7
[ reached getOption("max.print") -- omitted 20 rows ]CEA patients with MCP1_pg_ml_2015 and MCP1
Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ml_2015 and MCP1.
AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ml_2015) | !is.na(MCP1))
AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.combo, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test
n 161 1168
Hospital % (freq) St. Antonius, Nieuwegein 52.2 ( 84) 46.8 ( 547) 0.235
UMC Utrecht 47.8 ( 77) 53.2 ( 621)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
2002 10.6 ( 17) 4.8 ( 56)
2003 11.8 ( 19) 10.6 ( 124)
2004 19.9 ( 32) 12.2 ( 142)
2005 13.7 ( 22) 13.3 ( 155)
2006 8.7 ( 14) 9.9 ( 116)
2007 9.3 ( 15) 9.6 ( 112)
2008 6.2 ( 10) 6.8 ( 79)
2009 6.2 ( 10) 7.7 ( 90)
2010 4.3 ( 7) 6.9 ( 81)
2011 5.0 ( 8) 8.7 ( 102)
2012 4.3 ( 7) 7.6 ( 89)
2013 0.0 ( 0) 1.8 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 65.901 (9.051) 68.788 (9.077) <0.001
Gender % (freq) female 23.0 ( 37) 30.4 ( 355) 0.066
male 77.0 (124) 69.6 ( 813)
TC_finalCU (mean (SD)) 179.199 (45.274) 184.078 (48.333) 0.322
LDL_finalCU (mean (SD)) 104.132 (37.590) 109.761 (41.318) 0.206
HDL_finalCU (mean (SD)) 44.749 (14.890) 45.803 (18.219) 0.570
TG_finalCU (mean (SD)) 158.699 (87.584) 145.901 (83.176) 0.141
TC_final (mean (SD)) 4.641 (1.173) 4.768 (1.252) 0.322
LDL_final (mean (SD)) 2.697 (0.974) 2.843 (1.070) 0.206
HDL_final (mean (SD)) 1.159 (0.386) 1.186 (0.472) 0.570
TG_final (mean (SD)) 1.793 (0.990) 1.649 (0.940) 0.141
hsCRP_plasma (mean (SD)) 6.846 (21.838) 16.179 (110.739) 0.394
systolic (mean (SD)) 152.838 (24.600) 155.713 (26.406) 0.230
diastoli (mean (SD)) 80.824 (12.855) 82.863 (13.542) 0.097
GFR_MDRD (mean (SD)) 70.440 (19.793) 71.890 (20.127) 0.400
BMI (mean (SD)) 26.626 (3.572) 26.352 (3.765) 0.392
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
Normal kidney function 14.9 ( 24) 17.4 ( 203)
CKD 2 (Mild) 50.9 ( 82) 53.3 ( 623)
CKD 3 (Moderate) 29.8 ( 48) 24.0 ( 280)
CKD 4 (Severe) 0.0 ( 0) 1.3 ( 15)
CKD 5 (Failure) 0.6 ( 1) 0.4 ( 5)
<NA> 3.7 ( 6) 3.6 ( 42)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
Underweight 1.2 ( 2) 0.9 ( 11)
Normal 32.3 ( 52) 35.5 ( 415)
Overweight 49.7 ( 80) 45.6 ( 533)
Obese 13.7 ( 22) 13.1 ( 153)
<NA> 3.1 ( 5) 4.8 ( 56)
SmokerStatus % (freq) Current smoker 29.2 ( 47) 36.0 ( 421) 0.070
Ex-smoker 56.5 ( 91) 45.6 ( 533)
Never smoked 11.8 ( 19) 14.1 ( 165)
<NA> 2.5 ( 4) 4.2 ( 49)
AlcoholUse % (freq) No 38.5 ( 62) 33.6 ( 393) 0.213
Yes 59.6 ( 96) 62.2 ( 726)
<NA> 1.9 ( 3) 4.2 ( 49)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 78.3 (126) 77.2 ( 902) 0.846
Diabetes 21.7 ( 35) 22.8 ( 266)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 25.5 ( 41) 26.6 ( 311)
yes 73.9 (119) 71.3 ( 833)
<NA> 0.6 ( 1) 2.1 ( 24)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 32.3 ( 52) 33.0 ( 385)
yes 66.5 (107) 64.5 ( 753)
<NA> 1.2 ( 2) 2.6 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 11.2 ( 18) 14.1 ( 165)
yes 88.8 (143) 85.9 (1003)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 15.5 ( 25) 22.8 ( 266)
yes 83.9 (135) 77.1 ( 900)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 89.4 (144) 87.9 (1027)
yes 9.9 ( 16) 11.9 ( 139)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 6.2 ( 10) 10.9 ( 127)
yes 92.5 (149) 88.8 (1037)
<NA> 1.2 ( 2) 0.3 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN
no 17.4 ( 28) 23.1 ( 270)
yes 82.0 (132) 76.7 ( 896)
<NA> 0.6 ( 1) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN
No stroke diagnosed 80.1 (129) 75.5 ( 882)
Stroke diagnosed 13.7 ( 22) 19.1 ( 223)
<NA> 6.2 ( 10) 5.4 ( 63)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN
Asymptomatic 100.0 (161) 0.0 ( 0)
TIA 0.0 ( 0) 46.5 ( 543)
minor stroke 0.0 ( 0) 17.1 ( 200)
Major stroke 0.0 ( 0) 11.6 ( 136)
Amaurosis fugax 0.0 ( 0) 17.0 ( 198)
Four vessel disease 0.0 ( 0) 2.1 ( 25)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 16)
Symptomatic, but aspecific symtoms 0.0 ( 0) 3.1 ( 36)
Contralateral symptomatic occlusion 0.0 ( 0) 0.6 ( 7)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001
Ocular 0.0 ( 0) 17.0 ( 199)
Other 0.0 ( 0) 5.9 ( 69)
Retinal infarction 0.0 ( 0) 1.6 ( 19)
Stroke 0.0 ( 0) 28.8 ( 336)
TIA 0.0 ( 0) 46.7 ( 545)
AsymptSympt % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001
Ocular and others 0.0 ( 0) 24.6 ( 287)
Symptomatic 0.0 ( 0) 75.4 ( 881)
AsymptSympt2G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001
Symptomatic 0.0 ( 0) 100.0 (1168)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN
de novo 93.2 (150) 95.0 (1110)
restenosis 3.7 ( 6) 3.1 ( 36)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 1.9 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN
0-49% 0.0 ( 0) 0.6 ( 7)
50-70% 2.5 ( 4) 6.2 ( 73)
70-90% 50.9 ( 82) 44.5 ( 520)
90-99% 42.9 ( 69) 43.2 ( 505)
100% (Occlusion) 0.0 ( 0) 0.9 ( 11)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.6 ( 1) 0.4 ( 5)
70-99% 0.0 ( 0) 1.2 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 2.8 ( 33)
MedHx_CVD % (freq) No 37.3 ( 60) 36.7 ( 429) 0.964
yes 62.7 (101) 63.3 ( 739)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN
No history CAD 59.0 ( 95) 69.1 ( 807)
History CAD 41.0 ( 66) 30.9 ( 361)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN
no 73.9 (119) 79.9 ( 933)
yes 26.1 ( 42) 20.1 ( 235)
Peripheral.interv % (freq) no 72.7 (117) 83.0 ( 969) 0.004
yes 27.3 ( 44) 16.8 ( 196)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN
No composite endpoints 68.3 (110) 73.8 ( 862)
Composite endpoints 31.7 ( 51) 25.3 ( 295)
<NA> 0.0 ( 0) 0.9 ( 11)
EP_composite_time (mean (SD)) 2.579 (0.961) 2.611 (1.129) 0.735
macmean0 (mean (SD)) 0.802 (1.072) 0.821 (1.274) 0.864
smcmean0 (mean (SD)) 2.445 (2.594) 1.924 (2.232) 0.007
Macrophages.bin % (freq) no/minor 50.3 ( 81) 45.8 ( 535) 0.314
moderate/heavy 49.1 ( 79) 52.2 ( 610)
<NA> 0.6 ( 1) 2.0 ( 23)
SMC.bin % (freq) no/minor 21.7 ( 35) 32.4 ( 379) 0.018
moderate/heavy 77.0 (124) 65.8 ( 769)
<NA> 1.2 ( 2) 1.7 ( 20)
neutrophils (mean (SD)) 133.447 (437.032) 158.140 (448.512) 0.754
Mast_cells_plaque (mean (SD)) 123.389 (135.924) 173.244 (168.601) 0.097
IPH.bin % (freq) no 39.1 ( 63) 36.5 ( 426) 0.526
yes 60.2 ( 97) 61.9 ( 723)
<NA> 0.6 ( 1) 1.6 ( 19)
vessel_density_averaged (mean (SD)) 8.837 (6.727) 8.434 (6.386) 0.474
Stratified by AsymptSympt2G
Missing
n
Hospital % (freq) 0.0
ORyear % (freq) 0.0
Age (mean (SD)) 0.0
Gender % (freq) 0.0
TC_finalCU (mean (SD)) 32.8
LDL_finalCU (mean (SD)) 39.9
HDL_finalCU (mean (SD)) 36.2
TG_finalCU (mean (SD)) 35.7
TC_final (mean (SD)) 32.8
LDL_final (mean (SD)) 39.9
HDL_final (mean (SD)) 36.2
TG_final (mean (SD)) 35.7
hsCRP_plasma (mean (SD)) 40.6
systolic (mean (SD)) 13.5
diastoli (mean (SD)) 13.5
GFR_MDRD (mean (SD)) 3.5
BMI (mean (SD)) 4.4
KDOQI % (freq) 3.6
BMI_WHO % (freq) 4.6
SmokerStatus % (freq) 4.0
AlcoholUse % (freq) 3.9
DiabetesStatus % (freq) 0.0
Hypertension.selfreport % (freq) 1.9
Hypertension.selfreportdrug % (freq) 2.4
Hypertension.composite % (freq) 0.0
Hypertension.drugs % (freq) 0.2
Med.anticoagulants % (freq) 0.2
Med.all.antiplatelet % (freq) 0.5
Med.Statin.LLD % (freq) 0.2
Stroke_Dx % (freq) 5.5
sympt % (freq) 0.0
Symptoms.5G % (freq) 0.0
AsymptSympt % (freq) 0.0
AsymptSympt2G % (freq) 0.0
restenos % (freq) 2.0
stenose % (freq) 2.9
MedHx_CVD % (freq) 0.0
CAD_history % (freq) 0.0
PAOD % (freq) 0.0
Peripheral.interv % (freq) 0.2
EP_composite % (freq) 0.8
EP_composite_time (mean (SD)) 1.0
macmean0 (mean (SD)) 2.2
smcmean0 (mean (SD)) 2.5
Macrophages.bin % (freq) 1.8
SMC.bin % (freq) 1.7
neutrophils (mean (SD)) 81.0
Mast_cells_plaque (mean (SD)) 83.7
IPH.bin % (freq) 1.5
vessel_density_averaged (mean (SD)) 8.0
[ reached getOption("max.print") -- omitted 20 rows ]Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
AEDB.CEA.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
AEDB.CEA.subset.AsymptSympt.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline_Sympt")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
AEDB.CEA.subset.combo.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "subsetAEDB_Baseline")
Data exploration
Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, and inverse-rank normal transformation (standardise). We know that the proteins are not normally distributed and therefore we will standardise them as follows:
z = ( x - μ ) / σ
Where for each sample, x equals the value of the variable, μ (mu) equals the mean of x, and σ (sigma) equals the standard deviation of x.
MCP1 plaque levels: experiment 2
We will explore the plaque levels. As noted above, we will use MCP1_pg_ml_2015, this was experiment 2 in 2015 on the LUMINEX-platform and measurements are in pg/mL.
summary(AEDB.CEA$MCP1_pg_ml_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.66 101.34 298.76 600.44 770.98 10181.08 1224 do.call(rbind , by(AEDB.CEA$MCP1_pg_ml_2015, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 9.36 71.1650 152.220 405.1822 537.9100 2669.59 139
Symptomatic 0.66 114.9425 314.625 624.3948 792.4225 10181.08 1085library(patchwork)
Attaching package: ‘patchwork’
The following object is masked from ‘package:MASS’:
areap1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_pg_ml_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ml_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ml_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p3)MCP1 plaque levels: experiment 1
We will explore the plaque levels. As noted above, we will use MCP1, this was experiment 1 on the LUMINEX-platform and measurements are in pg/mL.
# summary(AEDB.CEA$MCP1)
#
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
#
attach(AEDB.CEA)
AEDB.CEA$MCP1[MCP1 == 0] <- NA
detach(AEDB.CEA)
summary(AEDB.CEA$MCP1) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
3.865 58.057 103.811 137.960 180.297 926.273 1867 do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 15.578813 45.31926 77.84731 119.4878 126.1851 846.5306 184
Symptomatic 3.864774 60.54905 111.87004 141.3406 186.4375 926.2729 1683p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p3)
Correlations between MCP1 plaque levels and transformations
Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2.
p1 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ml_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "experiment 1",
ylab = "experiment 2",
title = "MCP1 plaque levels, INT, [pg/mL]",
ggtheme = theme_minimal())
p1
Preliminary conclusion data exploration
Based on the inverse-rank normal transformation we conclude there are no outliers and the data approximates a normal distribution. We will apply inverse-rank normal transformation on all proteins and focus the analysis on MCP1 in plaque.
Analyses
The analyses are focused on three elements:
- plaque vulnerability phenotypes
- clinical status at inclusion (symptoms)
- secondary clinical outcome during three (3) years of follow-up
Covariates & other variables
- Age (continuous in 1-year increment). [
Age] - Sex (male vs. female). [
Gender] - Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [
Hypertension.composite] - Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [
DiabetesStatus] - Smoking (current, ex-, never). [
SmokerStatus] - LDL-C levels (continuous). [
LDL_final] - Use of lipid-lowering drugs. [
Med.Statin.LLD] - Use of antiplatelet drugs. [
Med.all.antiplatelet] - eGFR (continuous). [
GFR_MDRD] - BMI (continuous). [
BMI] - History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [
MedHx_CVD] combination of [CAD_history,Stroke_history,Peripheral.interv] - Level of stenosis (50-70% vs. 70-99%). [
stenose] - Year of surgery [
ORdate_year] as we discovered in Van Lammeren et al. the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.
Models
We will analyze the data through four different models
- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)
A. Cross-sectional analysis plaque phenotypes
In the cross-sectional analysis of plaque MCP1 levels we will focus on the following plaque vulnerability phenotypes:
- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)
Continous traits
We inspect the plaque characteristics, and inverse-rank normal transformation continuous phenotypes.
# macrophages
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7671 0.9967 15.1000 720 min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))
Minimum value % macrophages: 0.AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)
ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# smooth muscle cells
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7671 0.9967 15.1000 720 min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))
Minimum value % smooth muscle cells: 0.AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)
ggpubr::gghistogram(AEDB.CEA, "SMC_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# vessel density
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$vessel_density_averaged) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 4.000 7.000 8.318 11.300 48.000 850 min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity[1] 0cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))
Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "natural log-transformed number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "inverse-rank normalized number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed (‘unstable’) as compared to ‘stable’.
AEDB.CEA$MAC_SMC_ratio <- AEDB.CEA$macmean0 / AEDB.CEA$smcmean0
AEDB.CEA$MAC_SMC_ratio_rank <- qnorm((rank(AEDB.CEA$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MAC_SMC_ratio)))
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Macrophages_rank) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-2.3161 -0.6703 0.0000 0.0020 0.6745 3.4375 720 summary(AEDB.CEA$SMC_rank) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-2.6939 -0.6736 0.0015 0.0006 0.6740 3.4368 724 summary(AEDB.CEA$MAC_SMC_ratio_rank) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-2.3364 -0.6740 0.0000 0.0013 0.6740 2.7533 728 ggpubr::gghistogram(AEDB.CEA, "MAC_SMC_ratio_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "macrophages/smooth muscle cells ratio",
xlab = "inverse-rank normalized",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
Binary traits
# calcification
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Calc.bin) no/minor moderate/heavy NA's
1007 850 566 contrasts(AEDB.CEA$Calc.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)
df <- AEDB.CEA %>%
filter(!is.na(CalcificationPlaque)) %>%
group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Calcification",
xlab = "calcification",
ggtheme = theme_minimal())
rm(df)
# collagen
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Collagen.bin) no/minor moderate/heavy NA's
382 1469 572 contrasts(AEDB.CEA$Collagen.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)
df <- AEDB.CEA %>%
filter(!is.na(CollagenPlaque)) %>%
group_by(Gender, CollagenPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Collagen",
xlab = "collagen",
ggtheme = theme_minimal())
rm(df)
# fat 10%
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Fat.bin_10) <10% >10% NA's
542 1316 565 contrasts(AEDB.CEA$Fat.bin_10) >10%
<10% 0
>10% 1AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)
df <- AEDB.CEA %>%
filter(!is.na(Fat10Perc)) %>%
group_by(Gender, Fat10Perc) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque fat",
xlab = "intraplaque fat",
ggtheme = theme_minimal())
rm(df)
# macrophages binned
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Macrophages.bin) no/minor moderate/heavy NA's
847 992 584 contrasts(AEDB.CEA$Macrophages.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$MAC_binned <- as.factor(AEDB.CEA$Macrophages.bin)
df <- AEDB.CEA %>%
filter(!is.na(MAC_binned)) %>%
group_by(Gender, MAC_binned) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Macrophages (binned)",
xlab = "Macrophages",
ggtheme = theme_minimal())
rm(df)
# macrophages grouped
cat("Summary of data.\n")Summary of data.AEDB.CEA$macrophages <- as.factor(AEDB.CEA$macrophages)
summary(AEDB.CEA$macrophages)-888 0 1 2 3 NA's
6 173 674 786 206 578 contrasts(AEDB.CEA$macrophages) 0 1 2 3
-888 0 0 0 0
0 1 0 0 0
1 0 1 0 0
2 0 0 1 0
3 0 0 0 1AEDB.CEA$MAC_grouped <- as.factor(AEDB.CEA$macrophages)
df <- AEDB.CEA %>%
filter(!is.na(MAC_grouped)) %>%
group_by(Gender, MAC_grouped) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "MAC_grouped", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Macrophages (grouped)",
xlab = "Macrophages",
ggtheme = theme_minimal())
rm(df)
# SMC binned
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$SMC.bin) no/minor moderate/heavy NA's
602 1244 577 contrasts(AEDB.CEA$SMC.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$SMC_binned <- as.factor(AEDB.CEA$SMC.bin)
df <- AEDB.CEA %>%
filter(!is.na(SMC_binned)) %>%
group_by(Gender, SMC_binned) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "SMC (binned)",
xlab = "SMC",
ggtheme = theme_minimal())
rm(df)
# SMC grouped
cat("Summary of data.\n")Summary of data.AEDB.CEA$smc <- as.factor(AEDB.CEA$smc)
summary(AEDB.CEA$smc)-888 0 1 2 3 NA's
4 44 558 908 336 573 contrasts(AEDB.CEA$smc) 0 1 2 3
-888 0 0 0 0
0 1 0 0 0
1 0 1 0 0
2 0 0 1 0
3 0 0 0 1AEDB.CEA$SMC_grouped <- as.factor(AEDB.CEA$smc)
df <- AEDB.CEA %>%
filter(!is.na(SMC_grouped)) %>%
group_by(Gender, SMC_grouped) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "SMC_grouped", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "SMC (grouped)",
xlab = "SMC",
ggtheme = theme_minimal())
rm(df)
# IPH
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$IPH.bin) no yes NA's
746 1108 569 contrasts(AEDB.CEA$IPH.bin) yes
no 0
yes 1AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)
df <- AEDB.CEA %>%
filter(!is.na(IPH)) %>%
group_by(Gender, IPH) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "IPH", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque hemorrhage",
xlab = "intraplaque hemorrhage",
ggtheme = theme_minimal())
rm(df)
# Symptoms
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$AsymptSympt) Asymptomatic Ocular and others Symptomatic
270 541 1612 contrasts(AEDB.CEA$AsymptSympt) Ocular and others Symptomatic
Asymptomatic 0 0
Ocular and others 1 0
Symptomatic 0 1AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)
df <- AEDB.CEA %>%
filter(!is.na(AsymptSympt)) %>%
group_by(Gender, AsymptSympt) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Symptoms",
xlab = "symptoms",
ggtheme = theme_minimal())
rm(df)
Correlations between MCP1 plaque levels and surgery year
Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).
p1 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "experiment 1",
title = "MCP1 plaque levels, INT, [pg/mL]",
ggtheme = theme_minimal())
p1 
p2 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_pg_ml_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "experiment 2, [pg/mL]",
title = "MCP1 plaque levels, INT, [pg/mL]",
ggtheme = theme_minimal())
p2 
rm(p1, p2)
In this section we make some variables to assist with analysis.
AEDB.CEA.samplesize = nrow(AEDB.CEA)
TRAITS.PROTEIN.RANK = c("MCP1_pg_ml_2015_rank", "MCP1_rank")
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "MAC_SMC_ratio_rank", "VesselDensity_rank")
TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
"MAC_binned", "SMC_binned")
# "Hospital",
# "Age", "Gender",
# "TC_final", "LDL_final", "HDL_final", "TG_final",
# "systolic", "diastoli", "GFR_MDRD", "BMI",
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite",
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin",
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015",
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"
# 1. Age (continuous in 1-year increment). [Age]
# 2. Sex (male vs. female). [Gender]
# 3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5. Smoking (current, ex-, never). [SmokerCurrent]
# 6. LDL-C levels (continuous). [LDL_final]
# 7. Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8. Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9. eGFR (continuous). [GFR_MDRD]
# 10. BMI (continuous). [BMI]
# 11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12. Level of stenosis (50-70% vs. 70-99%). [stenose]
# Models
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
AEDB.CEA$ORdate_epoch <- as.numeric(AEDB.CEA$dateok)
AEDB.CEA$ORdate_year <- as.numeric(year(AEDB.CEA$dateok))
cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`summary(AEDB.CEA$ORdate_epoch) Min. 1st Qu. Median Mean 3rd Qu. Max.
11770 13132 14518 14567 15860 18250 cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")
Summary of 'year of surgery' in 'years' (); coded as `factor()`table(AEDB.CEA$ORdate_year)
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
81 157 190 185 183 152 138 182 159 164 176 149 163 76 85 65 66 52 COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
# COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")
COVARIATES_M2 = c(COVARIATES_M1,
"Hypertension.composite", "DiabetesStatus",
"SmokerStatus",
# "SmokerCurrent",
"Med.Statin.LLD", "Med.all.antiplatelet",
"GFR_MDRD", "BMI",
# "CAD_history", "Stroke_history", "Peripheral.interv",
"MedHx_CVD",
"stenose")
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")
# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Quantitative plaque traits
Analysis of continuous/quantitative plaque traits as a function of plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ml_2015_rank.
- processing Macrophages_rankNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(PROTEIN)` instead of `PROTEIN` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Note: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(TRAIT)` instead of `TRAIT` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Note: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(COVARIATES_M1)` instead of `COVARIATES_M1` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
-254.43741 0.06422 0.32420 0.12666
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.98066 -0.58281 -0.01477 0.58485 3.01698
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.535e+02 1.846e+01 -13.732 < 2e-16 ***
currentDF[, TRAIT] 6.552e-02 2.761e-02 2.373 0.0178 *
Age 1.944e-03 2.918e-03 0.666 0.5055
Gendermale 3.236e-01 5.775e-02 5.603 2.63e-08 ***
ORdate_year 1.261e-01 9.207e-03 13.698 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9087 on 1166 degrees of freedom
Multiple R-squared: 0.1648, Adjusted R-squared: 0.162
F-statistic: 57.54 on 4 and 1166 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.065515
Standard error............: 0.027613
Odds ratio (effect size)..: 1.068
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.127
T-value...................: 2.372657
P-value...................: 0.01782216
R^2.......................: 0.164841
Adjusted r^2..............: 0.161976
Sample size of AE DB......: 2423
Sample size of model......: 1171
Missing data %............: 51.67148
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
-232.0776 -0.0943 0.3013 0.1155
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.15076 -0.58587 -0.02393 0.55488 3.09880
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.322e+02 1.827e+01 -12.713 < 2e-16 ***
currentDF[, TRAIT] -9.502e-02 2.851e-02 -3.333 0.000887 ***
Age -4.616e-04 2.944e-03 -0.157 0.875435
Gendermale 3.012e-01 5.795e-02 5.198 2.38e-07 ***
ORdate_year 1.156e-01 9.109e-03 12.694 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.904 on 1162 degrees of freedom
Multiple R-squared: 0.1708, Adjusted R-squared: 0.168
F-statistic: 59.84 on 4 and 1162 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.095019
Standard error............: 0.02851
Odds ratio (effect size)..: 0.909
Lower 95% CI..............: 0.86
Upper 95% CI..............: 0.962
T-value...................: -3.332864
P-value...................: 0.0008866462
R^2.......................: 0.170817
Adjusted r^2..............: 0.167963
Sample size of AE DB......: 2423
Sample size of model......: 1167
Missing data %............: 51.83657
- processing MAC_SMC_ratio_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
-252.3972 0.1248 0.2862 0.1257
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.95999 -0.57823 -0.00289 0.55678 3.03063
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.520e+02 1.785e+01 -14.117 < 2e-16 ***
currentDF[, TRAIT] 1.246e-01 2.747e-02 4.537 6.31e-06 ***
Age 7.136e-04 2.899e-03 0.246 0.806
Gendermale 2.861e-01 5.803e-02 4.931 9.38e-07 ***
ORdate_year 1.254e-01 8.904e-03 14.085 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9011 on 1160 degrees of freedom
Multiple R-squared: 0.1775, Adjusted R-squared: 0.1747
F-statistic: 62.59 on 4 and 1160 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_SMC_ratio_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MAC_SMC_ratio_rank
Effect size...............: 0.124615
Standard error............: 0.027468
Odds ratio (effect size)..: 1.133
Lower 95% CI..............: 1.073
Upper 95% CI..............: 1.195
T-value...................: 4.536768
P-value...................: 6.305959e-06
R^2.......................: 0.17752
Adjusted r^2..............: 0.174684
Sample size of AE DB......: 2423
Sample size of model......: 1165
Missing data %............: 51.91911
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
-228.08372 -0.06221 0.33587 0.11352
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.04767 -0.60662 0.00131 0.57990 3.05690
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.268e+02 1.916e+01 -11.838 < 2e-16 ***
currentDF[, TRAIT] -6.247e-02 2.862e-02 -2.183 0.0293 *
Age 1.921e-03 3.059e-03 0.628 0.5302
Gendermale 3.356e-01 6.036e-02 5.561 3.38e-08 ***
ORdate_year 1.128e-01 9.561e-03 11.801 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9201 on 1090 degrees of freedom
Multiple R-squared: 0.1558, Adjusted R-squared: 0.1527
F-statistic: 50.28 on 4 and 1090 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.062472
Standard error............: 0.02862
Odds ratio (effect size)..: 0.939
Lower 95% CI..............: 0.888
Upper 95% CI..............: 0.994
T-value...................: -2.182858
P-value...................: 0.02925907
R^2.......................: 0.155768
Adjusted r^2..............: 0.15267
Sample size of AE DB......: 2423
Sample size of model......: 1095
Missing data %............: 54.80809
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
422.8331 0.1222 0.2600 -0.2111
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4317 -0.6291 -0.0261 0.6543 2.8355
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 414.987584 75.061156 5.529 4.99e-08 ***
currentDF[, TRAIT] 0.121339 0.038035 3.190 0.0015 **
Age -0.006268 0.004724 -1.327 0.1851
Gendermale 0.263235 0.090556 2.907 0.0038 **
ORdate_year -0.206979 0.037471 -5.524 5.13e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9598 on 550 degrees of freedom
Multiple R-squared: 0.0847, Adjusted R-squared: 0.07804
F-statistic: 12.72 on 4 and 550 DF, p-value: 6.54e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.121339
Standard error............: 0.038035
Odds ratio (effect size)..: 1.129
Lower 95% CI..............: 1.048
Upper 95% CI..............: 1.216
T-value...................: 3.19016
P-value...................: 0.001502979
R^2.......................: 0.084699
Adjusted r^2..............: 0.078042
Sample size of AE DB......: 2423
Sample size of model......: 555
Missing data %............: 77.09451
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
485.13156 -0.22645 -0.01251 0.22171 -0.24174
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2040 -0.6017 -0.0439 0.6538 2.7241
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 485.131555 74.828140 6.483 2.01e-10 ***
currentDF[, TRAIT] -0.226449 0.039572 -5.722 1.73e-08 ***
Age -0.012514 0.004716 -2.654 0.0082 **
Gendermale 0.221712 0.089045 2.490 0.0131 *
ORdate_year -0.241741 0.037348 -6.473 2.14e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9371 on 547 degrees of freedom
Multiple R-squared: 0.1219, Adjusted R-squared: 0.1155
F-statistic: 18.98 on 4 and 547 DF, p-value: 1.253e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.226449
Standard error............: 0.039572
Odds ratio (effect size)..: 0.797
Lower 95% CI..............: 0.738
Upper 95% CI..............: 0.862
T-value...................: -5.722399
P-value...................: 1.731767e-08
R^2.......................: 0.121879
Adjusted r^2..............: 0.115458
Sample size of AE DB......: 2423
Sample size of model......: 552
Missing data %............: 77.21832
- processing MAC_SMC_ratio_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
469.90060 0.22103 -0.01003 0.21847 -0.23424
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3533 -0.5974 -0.0586 0.6573 2.9781
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 469.90060 73.95935 6.353 4.44e-10 ***
currentDF[, TRAIT] 0.22102 0.03612 6.119 1.80e-09 ***
Age -0.01003 0.00464 -2.162 0.0311 *
Gendermale 0.21847 0.08873 2.462 0.0141 *
ORdate_year -0.23424 0.03692 -6.345 4.68e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.933 on 546 degrees of freedom
Multiple R-squared: 0.1294, Adjusted R-squared: 0.123
F-statistic: 20.29 on 4 and 546 DF, p-value: 1.346e-15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_SMC_ratio_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MAC_SMC_ratio_rank
Effect size...............: 0.221025
Standard error............: 0.036122
Odds ratio (effect size)..: 1.247
Lower 95% CI..............: 1.162
Upper 95% CI..............: 1.339
T-value...................: 6.118872
P-value...................: 1.799303e-09
R^2.......................: 0.129413
Adjusted r^2..............: 0.123035
Sample size of AE DB......: 2423
Sample size of model......: 551
Missing data %............: 77.2596
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
427.9795 0.2941 -0.2137
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4045 -0.5978 -0.0351 0.6466 2.6590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 407.491047 77.437980 5.262 2.06e-07 ***
currentDF[, TRAIT] -0.055611 0.050753 -1.096 0.27369
Age -0.006762 0.004796 -1.410 0.15917
Gendermale 0.296448 0.092030 3.221 0.00135 **
ORdate_year -0.203215 0.038660 -5.257 2.12e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9662 on 539 degrees of freedom
Multiple R-squared: 0.07477, Adjusted R-squared: 0.0679
F-statistic: 10.89 on 4 and 539 DF, p-value: 1.697e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.055611
Standard error............: 0.050753
Odds ratio (effect size)..: 0.946
Lower 95% CI..............: 0.856
Upper 95% CI..............: 1.045
T-value...................: -1.09571
P-value...................: 0.2736949
R^2.......................: 0.07477
Adjusted r^2..............: 0.067904
Sample size of AE DB......: 2423
Sample size of model......: 544
Missing data %............: 77.54849 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Binary plaque traits
Analysis of binary plaque traits as a function of plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ml_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age ORdate_year
310.77553 -0.34903 0.02309 -0.15567
Degrees of Freedom: 1180 Total (i.e. Null); 1177 Residual
Null Deviance: 1637
Residual Deviance: 1513 AIC: 1521
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8216 -1.0490 -0.6315 1.0837 2.0978
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 312.506897 44.478670 7.026 2.13e-12 ***
currentDF[, PROTEIN] -0.340451 0.068997 -4.934 8.04e-07 ***
Age 0.023128 0.006798 3.402 0.000669 ***
Gendermale -0.109930 0.134724 -0.816 0.414521
ORdate_year -0.156493 0.022190 -7.052 1.76e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1636.6 on 1180 degrees of freedom
Residual deviance: 1512.0 on 1176 degrees of freedom
AIC: 1522
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.340451
Standard error............: 0.068997
Odds ratio (effect size)..: 0.711
Lower 95% CI..............: 0.621
Upper 95% CI..............: 0.814
Z-value...................: -4.934311
P-value...................: 8.04341e-07
Hosmer and Lemeshow r^2...: 0.07616
Cox and Snell r^2.........: 0.100162
Nagelkerke's pseudo r^2...: 0.133573
Sample size of AE DB......: 2423
Sample size of model......: 1181
Missing data %............: 51.25877
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.3403 -0.2873
Degrees of Freedom: 1181 Total (i.e. Null); 1180 Residual
Null Deviance: 1217
Residual Deviance: 1202 AIC: 1206
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2054 0.5389 0.6456 0.7150 1.0194
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -17.700869 50.816422 -0.348 0.727593
currentDF[, PROTEIN] -0.304533 0.079925 -3.810 0.000139 ***
Age 0.004359 0.007869 0.554 0.579607
Gendermale 0.066303 0.158551 0.418 0.675814
ORdate_year 0.009316 0.025343 0.368 0.713171
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1217.1 on 1181 degrees of freedom
Residual deviance: 1200.8 on 1177 degrees of freedom
AIC: 1210.8
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.304533
Standard error............: 0.079925
Odds ratio (effect size)..: 0.737
Lower 95% CI..............: 0.631
Upper 95% CI..............: 0.863
Z-value...................: -3.810259
P-value...................: 0.0001388211
Hosmer and Lemeshow r^2...: 0.013321
Cox and Snell r^2.........: 0.013622
Nagelkerke's pseudo r^2...: 0.021189
Sample size of AE DB......: 2423
Sample size of model......: 1182
Missing data %............: 51.2175
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
467.88804 0.44852 0.01646 0.80595 -0.23342
Degrees of Freedom: 1181 Total (i.e. Null); 1177 Residual
Null Deviance: 1390
Residual Deviance: 1258 AIC: 1268
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6456 -0.9830 0.5993 0.7919 1.6326
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 467.888045 53.325556 8.774 < 2e-16 ***
currentDF[, PROTEIN] 0.448516 0.079848 5.617 1.94e-08 ***
Age 0.016457 0.007424 2.217 0.0266 *
Gendermale 0.805951 0.144389 5.582 2.38e-08 ***
ORdate_year -0.233421 0.026589 -8.779 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1390.3 on 1181 degrees of freedom
Residual deviance: 1258.3 on 1177 degrees of freedom
AIC: 1268.3
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.448516
Standard error............: 0.079848
Odds ratio (effect size)..: 1.566
Lower 95% CI..............: 1.339
Upper 95% CI..............: 1.831
Z-value...................: 5.617133
P-value...................: 1.941519e-08
Hosmer and Lemeshow r^2...: 0.094988
Cox and Snell r^2.........: 0.105714
Nagelkerke's pseudo r^2...: 0.152862
Sample size of AE DB......: 2423
Sample size of model......: 1182
Missing data %............: 51.2175
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year
381.3362 0.1826 0.6048 -0.1900
Degrees of Freedom: 1178 Total (i.e. Null); 1175 Residual
Null Deviance: 1578
Residual Deviance: 1483 AIC: 1491
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0163 -1.1800 0.7413 0.9609 1.6978
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 386.982789 46.879353 8.255 < 2e-16 ***
currentDF[, PROTEIN] 0.181469 0.069739 2.602 0.00926 **
Age 0.008978 0.006779 1.324 0.18537
Gendermale 0.603961 0.134706 4.484 7.34e-06 ***
ORdate_year -0.193095 0.023380 -8.259 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1578.0 on 1178 degrees of freedom
Residual deviance: 1481.4 on 1174 degrees of freedom
AIC: 1491.4
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.181469
Standard error............: 0.069739
Odds ratio (effect size)..: 1.199
Lower 95% CI..............: 1.046
Upper 95% CI..............: 1.375
Z-value...................: 2.602126
P-value...................: 0.009264778
Hosmer and Lemeshow r^2...: 0.061186
Cox and Snell r^2.........: 0.078628
Nagelkerke's pseudo r^2...: 0.106581
Sample size of AE DB......: 2423
Sample size of model......: 1179
Missing data %............: 51.34131
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year
261.8955 0.2204 0.5260 -0.1306
Degrees of Freedom: 1175 Total (i.e. Null); 1172 Residual
Null Deviance: 1629
Residual Deviance: 1571 AIC: 1579
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7993 -1.1493 0.8206 1.0983 1.6704
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 258.120368 44.216573 5.838 5.29e-09 ***
currentDF[, PROTEIN] 0.221792 0.066957 3.312 0.000925 ***
Age -0.006995 0.006559 -1.066 0.286220
Gendermale 0.528275 0.131558 4.016 5.93e-05 ***
ORdate_year -0.128524 0.022049 -5.829 5.57e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1629.3 on 1175 degrees of freedom
Residual deviance: 1570.2 on 1171 degrees of freedom
AIC: 1580.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.221792
Standard error............: 0.066957
Odds ratio (effect size)..: 1.248
Lower 95% CI..............: 1.095
Upper 95% CI..............: 1.423
Z-value...................: 3.312458
P-value...................: 0.0009247989
Hosmer and Lemeshow r^2...: 0.036292
Cox and Snell r^2.........: 0.049038
Nagelkerke's pseudo r^2...: 0.065402
Sample size of AE DB......: 2423
Sample size of model......: 1176
Missing data %............: 51.46513
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
2.83719 -0.30011 -0.02675 -0.29630
Degrees of Freedom: 1176 Total (i.e. Null); 1173 Residual
Null Deviance: 1470
Residual Deviance: 1425 AIC: 1433
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9616 -1.3190 0.7505 0.8931 1.3158
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 25.048652 45.710531 0.548 0.583703
currentDF[, PROTEIN] -0.287159 0.071979 -3.989 6.62e-05 ***
Age -0.026390 0.007197 -3.667 0.000246 ***
Gendermale -0.299149 0.144473 -2.071 0.038395 *
ORdate_year -0.011078 0.022797 -0.486 0.626996
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1470.1 on 1176 degrees of freedom
Residual deviance: 1425.2 on 1172 degrees of freedom
AIC: 1435.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.287159
Standard error............: 0.071979
Odds ratio (effect size)..: 0.75
Lower 95% CI..............: 0.652
Upper 95% CI..............: 0.864
Z-value...................: -3.989465
P-value...................: 6.622254e-05
Hosmer and Lemeshow r^2...: 0.030554
Cox and Snell r^2.........: 0.037444
Nagelkerke's pseudo r^2...: 0.0525
Sample size of AE DB......: 2423
Sample size of model......: 1177
Missing data %............: 51.42386
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) ORdate_year
-451.4488 0.2255
Degrees of Freedom: 555 Total (i.e. Null); 554 Residual
Null Deviance: 749.7
Residual Deviance: 741.7 AIC: 745.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6532 -1.2833 0.8799 1.0256 1.3391
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -404.18119 165.52876 -2.442 0.0146 *
currentDF[, PROTEIN] -0.09186 0.09068 -1.013 0.3110
Age 0.01195 0.01016 1.176 0.2397
Gendermale -0.15600 0.19729 -0.791 0.4291
ORdate_year 0.20155 0.08263 2.439 0.0147 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 749.67 on 555 degrees of freedom
Residual deviance: 738.30 on 551 degrees of freedom
AIC: 748.3
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.091864
Standard error............: 0.090683
Odds ratio (effect size)..: 0.912
Lower 95% CI..............: 0.764
Upper 95% CI..............: 1.09
Z-value...................: -1.013025
P-value...................: 0.3110484
Hosmer and Lemeshow r^2...: 0.015167
Cox and Snell r^2.........: 0.020242
Nagelkerke's pseudo r^2...: 0.027342
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year
-780.9134 -0.4799 0.3905
Degrees of Freedom: 553 Total (i.e. Null); 551 Residual
Null Deviance: 538
Residual Deviance: 498 AIC: 504
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3527 0.3688 0.5145 0.6775 1.4039
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -801.64188 214.85023 -3.731 0.000191 ***
currentDF[, PROTEIN] -0.48686 0.12206 -3.989 6.65e-05 ***
Age -0.01852 0.01355 -1.367 0.171532
Gendermale -0.14801 0.26231 -0.564 0.572575
ORdate_year 0.40152 0.10726 3.743 0.000182 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 537.98 on 553 degrees of freedom
Residual deviance: 495.64 on 549 degrees of freedom
AIC: 505.64
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.486864
Standard error............: 0.122064
Odds ratio (effect size)..: 0.615
Lower 95% CI..............: 0.484
Upper 95% CI..............: 0.781
Z-value...................: -3.988597
P-value...................: 6.646533e-05
Hosmer and Lemeshow r^2...: 0.0787
Cox and Snell r^2.........: 0.073577
Nagelkerke's pseudo r^2...: 0.118419
Sample size of AE DB......: 2423
Sample size of model......: 554
Missing data %............: 77.13578
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
1.2197 0.6668 0.5508
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 538.8
Residual Deviance: 497.2 AIC: 503.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4515 0.3661 0.5131 0.6573 1.4042
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.478e+02 2.145e+02 -1.156 0.248
currentDF[, PROTEIN] 6.953e-01 1.238e-01 5.617 1.94e-08 ***
Age 4.338e-03 1.299e-02 0.334 0.738
Gendermale 5.263e-01 2.365e-01 2.226 0.026 *
ORdate_year 1.242e-01 1.071e-01 1.160 0.246
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 538.82 on 555 degrees of freedom
Residual deviance: 495.66 on 551 degrees of freedom
AIC: 505.66
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.695328
Standard error............: 0.123786
Odds ratio (effect size)..: 2.004
Lower 95% CI..............: 1.573
Upper 95% CI..............: 2.555
Z-value...................: 5.617164
P-value...................: 1.941174e-08
Hosmer and Lemeshow r^2...: 0.080102
Cox and Snell r^2.........: 0.07469
Nagelkerke's pseudo r^2...: 0.120356
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-0.76646 0.02073 0.78990
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 611.8
Residual Deviance: 594.4 AIC: 600.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0029 0.5582 0.6468 0.7206 1.1969
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 15.339755 191.148455 0.080 0.936038
currentDF[, PROTEIN] 0.064053 0.104480 0.613 0.539831
Age 0.021375 0.011622 1.839 0.065905 .
Gendermale 0.774693 0.212151 3.652 0.000261 ***
ORdate_year -0.008053 0.095422 -0.084 0.932743
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 611.78 on 555 degrees of freedom
Residual deviance: 593.99 on 551 degrees of freedom
AIC: 603.99
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.064053
Standard error............: 0.10448
Odds ratio (effect size)..: 1.066
Lower 95% CI..............: 0.869
Upper 95% CI..............: 1.308
Z-value...................: 0.613068
P-value...................: 0.5398311
Hosmer and Lemeshow r^2...: 0.029089
Cox and Snell r^2.........: 0.031501
Nagelkerke's pseudo r^2...: 0.047211
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year
-823.9069 0.3857 0.3390 0.4112
Degrees of Freedom: 551 Total (i.e. Null); 548 Residual
Null Deviance: 749.1
Residual Deviance: 711.3 AIC: 719.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9353 -1.1973 0.7687 1.0163 1.6249
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -839.30600 175.54024 -4.781 1.74e-06 ***
currentDF[, PROTEIN] 0.37956 0.09495 3.998 6.40e-05 ***
Age -0.01358 0.01043 -1.302 0.1928
Gendermale 0.34867 0.19858 1.756 0.0791 .
ORdate_year 0.41935 0.08763 4.785 1.71e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 749.15 on 551 degrees of freedom
Residual deviance: 709.62 on 547 degrees of freedom
AIC: 719.62
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.379557
Standard error............: 0.094948
Odds ratio (effect size)..: 1.462
Lower 95% CI..............: 1.213
Upper 95% CI..............: 1.761
Z-value...................: 3.997506
P-value...................: 6.401333e-05
Hosmer and Lemeshow r^2...: 0.052768
Cox and Snell r^2.........: 0.06911
Nagelkerke's pseudo r^2...: 0.093064
Sample size of AE DB......: 2423
Sample size of model......: 552
Missing data %............: 77.21832
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
-331.25779 -0.44085 -0.03875 -0.59679 0.16731
Degrees of Freedom: 552 Total (i.e. Null); 548 Residual
Null Deviance: 667.1
Residual Deviance: 622.7 AIC: 632.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1704 -1.2187 0.6567 0.8334 1.4336
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -331.25779 184.63912 -1.794 0.07280 .
currentDF[, PROTEIN] -0.44085 0.10541 -4.182 2.88e-05 ***
Age -0.03875 0.01183 -3.276 0.00105 **
Gendermale -0.59679 0.23370 -2.554 0.01066 *
ORdate_year 0.16731 0.09218 1.815 0.06952 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 667.10 on 552 degrees of freedom
Residual deviance: 622.68 on 548 degrees of freedom
AIC: 632.68
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.440853
Standard error............: 0.105406
Odds ratio (effect size)..: 0.643
Lower 95% CI..............: 0.523
Upper 95% CI..............: 0.791
Z-value...................: -4.182437
P-value...................: 2.88401e-05
Hosmer and Lemeshow r^2...: 0.066596
Cox and Snell r^2.........: 0.077195
Nagelkerke's pseudo r^2...: 0.110168
Sample size of AE DB......: 2423
Sample size of model......: 553
Missing data %............: 77.17705 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Quantitative plaque traits
Analysis of continuous/quantitative plaque traits as a function of plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ml_2015_rank.
- processing Macrophages_rankNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(COVARIATES_M2)` instead of `COVARIATES_M2` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes Med.Statin.LLDyes
-260.49868 0.06069 0.29422 0.12984 -0.13333 -0.21052
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.90020 -0.57577 -0.02735 0.60041 3.00874
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.635e+02 2.097e+01 -12.563 < 2e-16 ***
currentDF[, TRAIT] 5.919e-02 2.995e-02 1.977 0.04835 *
Age 1.825e-03 3.597e-03 0.507 0.61205
Gendermale 3.196e-01 6.437e-02 4.964 8.1e-07 ***
ORdate_year 1.313e-01 1.046e-02 12.553 < 2e-16 ***
Hypertension.compositeyes -1.348e-01 8.759e-02 -1.539 0.12424
DiabetesStatusDiabetes -4.051e-02 7.024e-02 -0.577 0.56426
SmokerStatusEx-smoker -5.669e-02 6.636e-02 -0.854 0.39319
SmokerStatusNever smoked 2.783e-02 9.370e-02 0.297 0.76651
Med.Statin.LLDyes -2.103e-01 7.096e-02 -2.964 0.00311 **
Med.all.antiplateletyes 6.384e-02 9.880e-02 0.646 0.51833
GFR_MDRD -4.344e-04 1.529e-03 -0.284 0.77647
BMI -2.830e-03 8.022e-03 -0.353 0.72432
MedHx_CVDyes 5.212e-03 6.028e-02 0.086 0.93111
stenose50-70% -1.761e-01 3.922e-01 -0.449 0.65353
stenose70-90% 4.823e-03 3.765e-01 0.013 0.98978
stenose90-99% -3.883e-02 3.770e-01 -0.103 0.91800
stenose100% (Occlusion) -2.378e-01 4.841e-01 -0.491 0.62344
stenose50-99% -4.599e-01 5.907e-01 -0.779 0.43637
stenose70-99% -3.348e-01 5.299e-01 -0.632 0.52770
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.911 on 1001 degrees of freedom
Multiple R-squared: 0.1751, Adjusted R-squared: 0.1595
F-statistic: 11.19 on 19 and 1001 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.059193
Standard error............: 0.029945
Odds ratio (effect size)..: 1.061
Lower 95% CI..............: 1.001
Upper 95% CI..............: 1.125
T-value...................: 1.976704
P-value...................: 0.04834925
R^2.......................: 0.175135
Adjusted r^2..............: 0.159478
Sample size of AE DB......: 2423
Sample size of model......: 1021
Missing data %............: 57.86215
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes Med.Statin.LLDyes
-235.47131 -0.09684 0.26854 0.11737 -0.13276 -0.19071
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06717 -0.59118 -0.01529 0.56640 3.08389
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.401e+02 2.070e+01 -11.601 < 2e-16 ***
currentDF[, TRAIT] -9.359e-02 3.061e-02 -3.058 0.00229 **
Age -8.914e-05 3.618e-03 -0.025 0.98035
Gendermale 2.930e-01 6.519e-02 4.494 7.8e-06 ***
ORdate_year 1.197e-01 1.032e-02 11.598 < 2e-16 ***
Hypertension.compositeyes -1.246e-01 8.751e-02 -1.424 0.15478
DiabetesStatusDiabetes -4.013e-02 7.016e-02 -0.572 0.56745
SmokerStatusEx-smoker -5.388e-02 6.638e-02 -0.812 0.41716
SmokerStatusNever smoked 2.007e-02 9.357e-02 0.214 0.83020
Med.Statin.LLDyes -1.952e-01 7.081e-02 -2.756 0.00595 **
Med.all.antiplateletyes 4.912e-02 9.867e-02 0.498 0.61872
GFR_MDRD -1.574e-04 1.530e-03 -0.103 0.91809
BMI -3.457e-03 8.022e-03 -0.431 0.66658
MedHx_CVDyes 2.805e-03 6.029e-02 0.047 0.96290
stenose50-70% -1.347e-01 3.917e-01 -0.344 0.73094
stenose70-90% 5.330e-02 3.760e-01 0.142 0.88731
stenose90-99% 7.963e-03 3.766e-01 0.021 0.98313
stenose100% (Occlusion) -2.181e-01 4.833e-01 -0.451 0.65191
stenose50-99% -3.620e-01 5.902e-01 -0.613 0.53980
stenose70-99% -2.212e-01 5.293e-01 -0.418 0.67615
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9094 on 997 degrees of freedom
Multiple R-squared: 0.1788, Adjusted R-squared: 0.1631
F-statistic: 11.42 on 19 and 997 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.093585
Standard error............: 0.030608
Odds ratio (effect size)..: 0.911
Lower 95% CI..............: 0.858
Upper 95% CI..............: 0.967
T-value...................: -3.057532
P-value...................: 0.002291186
R^2.......................: 0.17877
Adjusted r^2..............: 0.16312
Sample size of AE DB......: 2423
Sample size of model......: 1017
Missing data %............: 58.02724
- processing MAC_SMC_ratio_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes Med.Statin.LLDyes
-257.0356 0.1286 0.2537 0.1281 -0.1341 -0.2143
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.87932 -0.58271 -0.01362 0.55751 3.02886
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.621e+02 2.028e+01 -12.925 < 2e-16 ***
currentDF[, TRAIT] 1.256e-01 2.973e-02 4.227 2.59e-05 ***
Age 1.042e-03 3.587e-03 0.291 0.77144
Gendermale 2.771e-01 6.516e-02 4.252 2.32e-05 ***
ORdate_year 1.306e-01 1.011e-02 12.916 < 2e-16 ***
Hypertension.compositeyes -1.291e-01 8.719e-02 -1.481 0.13889
DiabetesStatusDiabetes -3.981e-02 6.990e-02 -0.570 0.56914
SmokerStatusEx-smoker -5.634e-02 6.623e-02 -0.851 0.39520
SmokerStatusNever smoked 2.452e-03 9.355e-02 0.026 0.97910
Med.Statin.LLDyes -2.159e-01 7.072e-02 -3.053 0.00232 **
Med.all.antiplateletyes 6.327e-02 9.826e-02 0.644 0.51976
GFR_MDRD -2.742e-04 1.523e-03 -0.180 0.85713
BMI -3.173e-03 8.000e-03 -0.397 0.69171
MedHx_CVDyes -4.603e-03 6.019e-02 -0.076 0.93906
stenose50-70% -1.642e-01 3.904e-01 -0.421 0.67409
stenose70-90% 2.382e-02 3.744e-01 0.064 0.94928
stenose90-99% -4.335e-03 3.750e-01 -0.012 0.99078
stenose100% (Occlusion) -1.729e-01 4.818e-01 -0.359 0.71968
stenose50-99% -4.007e-01 5.875e-01 -0.682 0.49537
stenose70-99% -2.931e-01 5.268e-01 -0.556 0.57804
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.906 on 996 degrees of freedom
Multiple R-squared: 0.1857, Adjusted R-squared: 0.1701
F-statistic: 11.95 on 19 and 996 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_SMC_ratio_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MAC_SMC_ratio_rank
Effect size...............: 0.125649
Standard error............: 0.029729
Odds ratio (effect size)..: 1.134
Lower 95% CI..............: 1.07
Upper 95% CI..............: 1.202
T-value...................: 4.226533
P-value...................: 2.591157e-05
R^2.......................: 0.185682
Adjusted r^2..............: 0.170148
Sample size of AE DB......: 2423
Sample size of model......: 1016
Missing data %............: 58.06851
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes Med.Statin.LLDyes
-232.26066 -0.07525 0.29943 0.11576 -0.12715 -0.21061
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.96576 -0.59822 -0.00485 0.58684 3.01447
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.349e+02 2.150e+01 -10.925 < 2e-16 ***
currentDF[, TRAIT] -7.728e-02 3.096e-02 -2.496 0.01273 *
Age 1.892e-03 3.757e-03 0.504 0.61463
Gendermale 3.218e-01 6.710e-02 4.796 1.88e-06 ***
ORdate_year 1.171e-01 1.073e-02 10.912 < 2e-16 ***
Hypertension.compositeyes -1.342e-01 9.157e-02 -1.466 0.14306
DiabetesStatusDiabetes -5.770e-02 7.505e-02 -0.769 0.44220
SmokerStatusEx-smoker -4.766e-02 6.953e-02 -0.685 0.49321
SmokerStatusNever smoked 7.042e-03 9.807e-02 0.072 0.94278
Med.Statin.LLDyes -2.113e-01 7.346e-02 -2.877 0.00411 **
Med.all.antiplateletyes 7.706e-02 1.049e-01 0.735 0.46265
GFR_MDRD -6.724e-04 1.607e-03 -0.418 0.67581
BMI -2.815e-04 8.387e-03 -0.034 0.97323
MedHx_CVDyes 1.458e-02 6.296e-02 0.232 0.81686
stenose50-70% -2.957e-01 4.327e-01 -0.683 0.49451
stenose70-90% -5.852e-02 4.163e-01 -0.141 0.88824
stenose90-99% -1.188e-01 4.164e-01 -0.285 0.77550
stenose100% (Occlusion) -3.299e-01 5.170e-01 -0.638 0.52349
stenose50-99% -3.890e-01 6.203e-01 -0.627 0.53068
stenose70-99% -5.688e-01 6.212e-01 -0.916 0.36010
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9202 on 933 degrees of freedom
Multiple R-squared: 0.17, Adjusted R-squared: 0.1531
F-statistic: 10.06 on 19 and 933 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.077283
Standard error............: 0.030963
Odds ratio (effect size)..: 0.926
Lower 95% CI..............: 0.871
Upper 95% CI..............: 0.984
T-value...................: -2.496001
P-value...................: 0.01273189
R^2.......................: 0.169991
Adjusted r^2..............: 0.153088
Sample size of AE DB......: 2423
Sample size of model......: 953
Missing data %............: 60.66859
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
441.5668 0.1036 0.2776 -0.2203 -0.2432
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3192 -0.6253 0.0206 0.6596 2.6344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.154e+02 8.330e+01 4.987 8.60e-07 ***
currentDF[, TRAIT] 9.996e-02 4.118e-02 2.427 0.01558 *
Age -9.280e-03 5.771e-03 -1.608 0.10849
Gendermale 3.003e-01 1.002e-01 2.997 0.00287 **
ORdate_year -2.067e-01 4.157e-02 -4.972 9.23e-07 ***
Hypertension.compositeyes -2.382e-01 1.329e-01 -1.791 0.07385 .
DiabetesStatusDiabetes -6.961e-02 1.124e-01 -0.619 0.53601
SmokerStatusEx-smoker 8.372e-02 9.983e-02 0.839 0.40209
SmokerStatusNever smoked 2.684e-01 1.476e-01 1.819 0.06960 .
Med.Statin.LLDyes -1.509e-01 1.035e-01 -1.457 0.14568
Med.all.antiplateletyes 1.368e-01 1.587e-01 0.862 0.38929
GFR_MDRD -1.657e-04 2.489e-03 -0.067 0.94696
BMI -1.297e-02 1.190e-02 -1.090 0.27621
MedHx_CVDyes 2.265e-02 9.344e-02 0.242 0.80855
stenose50-70% -4.499e-01 6.185e-01 -0.727 0.46738
stenose70-90% -2.733e-01 5.744e-01 -0.476 0.63444
stenose90-99% -2.510e-01 5.728e-01 -0.438 0.66144
stenose100% (Occlusion) -9.705e-01 7.264e-01 -1.336 0.18217
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9725 on 479 degrees of freedom
Multiple R-squared: 0.1108, Adjusted R-squared: 0.0792
F-statistic: 3.51 on 17 and 479 DF, p-value: 3.139e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.099963
Standard error............: 0.041184
Odds ratio (effect size)..: 1.105
Lower 95% CI..............: 1.019
Upper 95% CI..............: 1.198
T-value...................: 2.427249
P-value...................: 0.01558152
R^2.......................: 0.110762
Adjusted r^2..............: 0.079203
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Hypertension.compositeyes
511.09348 -0.22506 -0.01132 0.23728 -0.25465 -0.19903
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1076 -0.6197 -0.0034 0.6938 2.4632
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.972e+02 8.244e+01 6.031 3.26e-09 ***
currentDF[, TRAIT] -2.277e-01 4.248e-02 -5.361 1.29e-07 ***
Age -1.495e-02 5.694e-03 -2.626 0.00891 **
Gendermale 2.430e-01 9.865e-02 2.463 0.01411 *
ORdate_year -2.474e-01 4.114e-02 -6.013 3.63e-09 ***
Hypertension.compositeyes -2.009e-01 1.285e-01 -1.563 0.11877
DiabetesStatusDiabetes -7.173e-02 1.095e-01 -0.655 0.51256
SmokerStatusEx-smoker 1.214e-01 9.718e-02 1.249 0.21228
SmokerStatusNever smoked 2.460e-01 1.435e-01 1.714 0.08712 .
Med.Statin.LLDyes -1.412e-01 1.011e-01 -1.397 0.16307
Med.all.antiplateletyes 1.136e-01 1.545e-01 0.735 0.46259
GFR_MDRD 2.431e-05 2.423e-03 0.010 0.99200
BMI -1.152e-02 1.157e-02 -0.996 0.31990
MedHx_CVDyes 1.966e-02 9.120e-02 0.216 0.82941
stenose50-70% -3.893e-01 6.021e-01 -0.647 0.51816
stenose70-90% -2.757e-01 5.591e-01 -0.493 0.62215
stenose90-99% -2.863e-01 5.573e-01 -0.514 0.60768
stenose100% (Occlusion) -1.135e+00 7.069e-01 -1.605 0.10909
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9465 on 477 degrees of freedom
Multiple R-squared: 0.1508, Adjusted R-squared: 0.1206
F-statistic: 4.983 on 17 and 477 DF, p-value: 5.352e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.22772
Standard error............: 0.042481
Odds ratio (effect size)..: 0.796
Lower 95% CI..............: 0.733
Upper 95% CI..............: 0.865
T-value...................: -5.360554
P-value...................: 1.29467e-07
R^2.......................: 0.150815
Adjusted r^2..............: 0.12055
Sample size of AE DB......: 2423
Sample size of model......: 495
Missing data %............: 79.57078
- processing MAC_SMC_ratio_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Hypertension.compositeyes
467.386591 0.217255 -0.009929 0.229963 -0.232837 -0.219314
Med.Statin.LLDyes
-0.151354
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2086 -0.6173 -0.0234 0.6782 2.6562
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.666e+02 8.141e+01 5.732 1.77e-08 ***
currentDF[, TRAIT] 2.178e-01 3.972e-02 5.485 6.72e-08 ***
Age -1.198e-02 5.637e-03 -2.125 0.0341 *
Gendermale 2.435e-01 9.849e-02 2.472 0.0138 *
ORdate_year -2.322e-01 4.063e-02 -5.714 1.94e-08 ***
Hypertension.compositeyes -2.375e-01 1.292e-01 -1.838 0.0666 .
DiabetesStatusDiabetes -6.732e-02 1.092e-01 -0.617 0.5378
SmokerStatusEx-smoker 7.512e-02 9.692e-02 0.775 0.4387
SmokerStatusNever smoked 2.284e-01 1.434e-01 1.593 0.1119
Med.Statin.LLDyes -1.495e-01 1.010e-01 -1.480 0.1394
Med.all.antiplateletyes 1.235e-01 1.541e-01 0.801 0.4235
GFR_MDRD 2.685e-04 2.420e-03 0.111 0.9117
BMI -1.346e-02 1.155e-02 -1.165 0.2445
MedHx_CVDyes -1.570e-02 9.116e-02 -0.172 0.8633
stenose50-70% -3.589e-01 6.008e-01 -0.597 0.5505
stenose70-90% -3.064e-01 5.577e-01 -0.549 0.5830
stenose90-99% -2.365e-01 5.560e-01 -0.425 0.6708
stenose100% (Occlusion) -1.056e+00 7.048e-01 -1.499 0.1346
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.944 on 476 degrees of freedom
Multiple R-squared: 0.155, Adjusted R-squared: 0.1248
F-statistic: 5.135 on 17 and 476 DF, p-value: 2.17e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_SMC_ratio_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MAC_SMC_ratio_rank
Effect size...............: 0.217845
Standard error............: 0.039715
Odds ratio (effect size)..: 1.243
Lower 95% CI..............: 1.15
Upper 95% CI..............: 1.344
T-value...................: 5.485155
P-value...................: 6.719875e-08
R^2.......................: 0.154968
Adjusted r^2..............: 0.124788
Sample size of AE DB......: 2423
Sample size of model......: 494
Missing data %............: 79.61205
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
463.7343 0.3103 -0.2315
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3250 -0.6474 0.0106 0.6218 2.5297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.218e+02 8.620e+01 4.893 1.37e-06 ***
currentDF[, TRAIT] -4.850e-02 5.471e-02 -0.887 0.3758
Age -9.513e-03 5.860e-03 -1.623 0.1052
Gendermale 3.285e-01 1.016e-01 3.235 0.0013 **
ORdate_year -2.100e-01 4.302e-02 -4.880 1.46e-06 ***
Hypertension.compositeyes -1.645e-01 1.355e-01 -1.214 0.2254
DiabetesStatusDiabetes -3.384e-02 1.148e-01 -0.295 0.7683
SmokerStatusEx-smoker 9.358e-02 1.014e-01 0.923 0.3567
SmokerStatusNever smoked 2.664e-01 1.497e-01 1.780 0.0758 .
Med.Statin.LLDyes -1.516e-01 1.052e-01 -1.442 0.1500
Med.all.antiplateletyes 1.357e-01 1.616e-01 0.840 0.4015
GFR_MDRD 6.123e-04 2.560e-03 0.239 0.8111
BMI -1.108e-02 1.208e-02 -0.917 0.3595
MedHx_CVDyes 3.668e-02 9.504e-02 0.386 0.6997
stenose50-70% -5.382e-01 6.214e-01 -0.866 0.3869
stenose70-90% -2.824e-01 5.775e-01 -0.489 0.6251
stenose90-99% -2.830e-01 5.758e-01 -0.491 0.6233
stenose100% (Occlusion) -1.043e+00 7.302e-01 -1.429 0.1538
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9771 on 469 degrees of freedom
Multiple R-squared: 0.1035, Adjusted R-squared: 0.07105
F-statistic: 3.186 on 17 and 469 DF, p-value: 2.002e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.048502
Standard error............: 0.054711
Odds ratio (effect size)..: 0.953
Lower 95% CI..............: 0.856
Upper 95% CI..............: 1.06
T-value...................: -0.886513
P-value...................: 0.3757955
R^2.......................: 0.103542
Adjusted r^2..............: 0.071048
Sample size of AE DB......: 2423
Sample size of model......: 487
Missing data %............: 79.90095 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Binary plaque traits
Analysis of binary plaque traits as a function of plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ml_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + ORdate_year + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
279.63668 -0.39476 0.02869 -0.14023 -0.41699 -0.46274
Degrees of Freedom: 1025 Total (i.e. Null); 1020 Residual
Null Deviance: 1420
Residual Deviance: 1308 AIC: 1320
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.863 -1.045 -0.608 1.074 2.130
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 257.844061 50.214433 5.135 2.82e-07 ***
currentDF[, PROTEIN] -0.402700 0.075598 -5.327 9.99e-08 ***
Age 0.029437 0.008526 3.453 0.000555 ***
Gendermale -0.003628 0.150954 -0.024 0.980824
ORdate_year -0.129597 0.025058 -5.172 2.32e-07 ***
Hypertension.compositeyes 0.283761 0.205758 1.379 0.167863
DiabetesStatusDiabetes -0.231758 0.164388 -1.410 0.158591
SmokerStatusEx-smoker -0.428649 0.155564 -2.755 0.005861 **
SmokerStatusNever smoked -0.502689 0.218425 -2.301 0.021368 *
Med.Statin.LLDyes -0.024746 0.165034 -0.150 0.880809
Med.all.antiplateletyes -0.052996 0.228424 -0.232 0.816534
GFR_MDRD 0.001570 0.003596 0.437 0.662445
BMI 0.021001 0.018720 1.122 0.261931
MedHx_CVDyes -0.040669 0.140089 -0.290 0.771581
stenose50-70% -0.823178 0.929921 -0.885 0.376042
stenose70-90% -0.353208 0.888418 -0.398 0.690948
stenose90-99% -0.317638 0.889813 -0.357 0.721113
stenose100% (Occlusion) 0.802490 1.222297 0.657 0.511475
stenose50-99% -14.187677 432.155336 -0.033 0.973810
stenose70-99% -0.451708 1.232581 -0.366 0.714012
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1420.3 on 1025 degrees of freedom
Residual deviance: 1294.3 on 1006 degrees of freedom
AIC: 1334.3
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.4027
Standard error............: 0.075598
Odds ratio (effect size)..: 0.669
Lower 95% CI..............: 0.576
Upper 95% CI..............: 0.775
Z-value...................: -5.32687
P-value...................: 9.991985e-08
Hosmer and Lemeshow r^2...: 0.088671
Cox and Snell r^2.........: 0.115512
Nagelkerke's pseudo r^2...: 0.154119
Sample size of AE DB......: 2423
Sample size of model......: 1026
Missing data %............: 57.6558
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked BMI
-82.29156 -0.31120 0.04123 -0.38704 -0.66237 0.04011
MedHx_CVDyes
0.24788
Degrees of Freedom: 1026 Total (i.e. Null); 1020 Residual
Null Deviance: 1049
Residual Deviance: 1021 AIC: 1035
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3321 0.4334 0.6127 0.7234 1.1601
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.689e+01 9.545e+02 -0.039 0.969169
currentDF[, PROTEIN] -3.104e-01 8.728e-02 -3.556 0.000377 ***
Age 1.523e-02 9.880e-03 1.541 0.123267
Gendermale 3.495e-02 1.787e-01 0.196 0.844926
ORdate_year 2.524e-02 2.928e-02 0.862 0.388654
Hypertension.compositeyes 2.493e-01 2.289e-01 1.089 0.276119
DiabetesStatusDiabetes 7.073e-02 1.979e-01 0.358 0.720716
SmokerStatusEx-smoker -4.601e-01 1.902e-01 -2.419 0.015552 *
SmokerStatusNever smoked -7.831e-01 2.492e-01 -3.142 0.001676 **
Med.Statin.LLDyes -8.771e-04 1.935e-01 -0.005 0.996383
Med.all.antiplateletyes 2.678e-01 2.604e-01 1.029 0.303619
GFR_MDRD 5.127e-03 4.253e-03 1.206 0.227994
BMI 4.255e-02 2.333e-02 1.824 0.068186 .
MedHx_CVDyes 2.191e-01 1.637e-01 1.339 0.180703
stenose50-70% -1.490e+01 9.527e+02 -0.016 0.987519
stenose70-90% -1.519e+01 9.527e+02 -0.016 0.987282
stenose90-99% -1.529e+01 9.527e+02 -0.016 0.987198
stenose100% (Occlusion) 3.121e-02 1.235e+03 0.000 0.999980
stenose50-99% -2.727e-01 1.512e+03 0.000 0.999856
stenose70-99% -1.484e+01 9.527e+02 -0.016 0.987573
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1048.5 on 1026 degrees of freedom
Residual deviance: 1006.6 on 1007 degrees of freedom
AIC: 1046.6
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.310382
Standard error............: 0.087283
Odds ratio (effect size)..: 0.733
Lower 95% CI..............: 0.618
Upper 95% CI..............: 0.87
Z-value...................: -3.55603
P-value...................: 0.0003765011
Hosmer and Lemeshow r^2...: 0.040027
Cox and Snell r^2.........: 0.040043
Nagelkerke's pseudo r^2...: 0.06259
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year SmokerStatusEx-smoker
469.37418 0.45540 0.01347 0.86049 -0.23404 -0.29641
SmokerStatusNever smoked
0.29609
Degrees of Freedom: 1026 Total (i.e. Null); 1020 Residual
Null Deviance: 1209
Residual Deviance: 1092 AIC: 1106
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6326 -0.9680 0.5857 0.7829 1.6994
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 484.672877 355.498772 1.363 0.1728
currentDF[, PROTEIN] 0.447618 0.086377 5.182 2.19e-07 ***
Age 0.015842 0.009283 1.707 0.0879 .
Gendermale 0.870259 0.163696 5.316 1.06e-07 ***
ORdate_year -0.235162 0.030057 -7.824 5.12e-15 ***
Hypertension.compositeyes -0.053017 0.230251 -0.230 0.8179
DiabetesStatusDiabetes -0.183150 0.181689 -1.008 0.3134
SmokerStatusEx-smoker -0.316266 0.174529 -1.812 0.0700 .
SmokerStatusNever smoked 0.288237 0.255876 1.126 0.2600
Med.Statin.LLDyes -0.049076 0.191425 -0.256 0.7977
Med.all.antiplateletyes 0.096033 0.259188 0.371 0.7110
GFR_MDRD 0.001989 0.003985 0.499 0.6176
BMI 0.005657 0.020516 0.276 0.7828
MedHx_CVDyes 0.093233 0.157184 0.593 0.5531
stenose50-70% -13.345325 350.353303 -0.038 0.9696
stenose70-90% -13.480479 350.353191 -0.038 0.9693
stenose90-99% -13.551667 350.353198 -0.039 0.9691
stenose100% (Occlusion) -14.180536 350.353926 -0.040 0.9677
stenose50-99% -14.930710 350.355179 -0.043 0.9660
stenose70-99% -13.822004 350.354155 -0.039 0.9685
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1209.2 on 1026 degrees of freedom
Residual deviance: 1082.9 on 1007 degrees of freedom
AIC: 1122.9
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.447618
Standard error............: 0.086377
Odds ratio (effect size)..: 1.565
Lower 95% CI..............: 1.321
Upper 95% CI..............: 1.853
Z-value...................: 5.182132
P-value...................: 2.193639e-07
Hosmer and Lemeshow r^2...: 0.104428
Cox and Snell r^2.........: 0.115698
Nagelkerke's pseudo r^2...: 0.167211
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year BMI MedHx_CVDyes
392.07230 0.18061 0.50160 -0.19581 0.02969 0.39131
Degrees of Freedom: 1024 Total (i.e. Null); 1019 Residual
Null Deviance: 1371
Residual Deviance: 1281 AIC: 1293
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1701 -1.1386 0.6982 0.9617 1.7551
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 413.430677 53.512385 7.726 1.11e-14 ***
currentDF[, PROTEIN] 0.180008 0.075716 2.377 0.017434 *
Age 0.010033 0.008479 1.183 0.236689
Gendermale 0.545667 0.152016 3.590 0.000331 ***
ORdate_year -0.206531 0.026700 -7.735 1.03e-14 ***
Hypertension.compositeyes -0.112636 0.207286 -0.543 0.586867
DiabetesStatusDiabetes -0.113506 0.165535 -0.686 0.492907
SmokerStatusEx-smoker -0.097149 0.158542 -0.613 0.540032
SmokerStatusNever smoked -0.141723 0.218025 -0.650 0.515672
Med.Statin.LLDyes -0.086084 0.170254 -0.506 0.613122
Med.all.antiplateletyes 0.103732 0.232980 0.445 0.656148
GFR_MDRD -0.002822 0.003613 -0.781 0.434817
BMI 0.036544 0.019035 1.920 0.054876 .
MedHx_CVDyes 0.365201 0.141153 2.587 0.009674 **
stenose50-70% -0.401826 0.943263 -0.426 0.670111
stenose70-90% -0.391222 0.909306 -0.430 0.667020
stenose90-99% -0.317648 0.911397 -0.349 0.727443
stenose100% (Occlusion) -0.745875 1.137193 -0.656 0.511894
stenose50-99% 0.123165 1.360810 0.091 0.927883
stenose70-99% 1.975403 1.425834 1.385 0.165919
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1371.2 on 1024 degrees of freedom
Residual deviance: 1268.6 on 1005 degrees of freedom
AIC: 1308.6
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.180008
Standard error............: 0.075716
Odds ratio (effect size)..: 1.197
Lower 95% CI..............: 1.032
Upper 95% CI..............: 1.389
Z-value...................: 2.377427
P-value...................: 0.0174339
Hosmer and Lemeshow r^2...: 0.074795
Cox and Snell r^2.........: 0.095211
Nagelkerke's pseudo r^2...: 0.12909
Sample size of AE DB......: 2423
Sample size of model......: 1025
Missing data %............: 57.69707
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
275.73528 0.22784 0.50126 -0.13759 0.05857 0.42075
Med.Statin.LLDyes Med.all.antiplateletyes
0.40047 -0.33729
Degrees of Freedom: 1022 Total (i.e. Null); 1015 Residual
Null Deviance: 1417
Residual Deviance: 1359 AIC: 1375
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.903 -1.130 0.754 1.097 1.650
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 296.254795 50.487265 5.868 4.41e-09 ***
currentDF[, PROTEIN] 0.230423 0.072546 3.176 0.001492 **
Age -0.009277 0.008169 -1.136 0.256081
Gendermale 0.507404 0.147625 3.437 0.000588 ***
ORdate_year -0.146960 0.025176 -5.837 5.30e-09 ***
Hypertension.compositeyes -0.018654 0.198510 -0.094 0.925134
DiabetesStatusDiabetes -0.028077 0.159451 -0.176 0.860227
SmokerStatusEx-smoker 0.113143 0.150717 0.751 0.452835
SmokerStatusNever smoked 0.509672 0.214258 2.379 0.017370 *
Med.Statin.LLDyes 0.378901 0.161396 2.348 0.018892 *
Med.all.antiplateletyes -0.419125 0.227897 -1.839 0.065901 .
GFR_MDRD 0.001073 0.003467 0.310 0.756838
BMI -0.016377 0.018281 -0.896 0.370346
MedHx_CVDyes 0.147434 0.136619 1.079 0.280517
stenose50-70% -0.680770 0.923420 -0.737 0.460985
stenose70-90% -0.715597 0.889289 -0.805 0.421002
stenose90-99% -0.798488 0.890683 -0.896 0.369991
stenose100% (Occlusion) -1.742277 1.149497 -1.516 0.129599
stenose50-99% -0.131148 1.354973 -0.097 0.922893
stenose70-99% 0.356396 1.186652 0.300 0.763919
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1417.5 on 1022 degrees of freedom
Residual deviance: 1350.2 on 1003 degrees of freedom
AIC: 1390.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.230423
Standard error............: 0.072546
Odds ratio (effect size)..: 1.259
Lower 95% CI..............: 1.092
Upper 95% CI..............: 1.452
Z-value...................: 3.176236
P-value...................: 0.001491997
Hosmer and Lemeshow r^2...: 0.047428
Cox and Snell r^2.........: 0.063603
Nagelkerke's pseudo r^2...: 0.084824
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale SmokerStatusEx-smoker SmokerStatusNever smoked
2.86041 -0.28454 -0.02413 -0.37184 -0.06239 -0.42455
Degrees of Freedom: 1022 Total (i.e. Null); 1017 Residual
Null Deviance: 1260
Residual Deviance: 1218 AIC: 1230
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0646 -1.2798 0.7265 0.8731 1.3692
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -21.133458 52.552076 -0.402 0.687579
currentDF[, PROTEIN] -0.284484 0.078214 -3.637 0.000276 ***
Age -0.021624 0.009007 -2.401 0.016362 *
Gendermale -0.400820 0.164887 -2.431 0.015062 *
ORdate_year 0.011380 0.026223 0.434 0.664310
Hypertension.compositeyes 0.176586 0.212257 0.832 0.405441
DiabetesStatusDiabetes 0.007316 0.171510 0.043 0.965977
SmokerStatusEx-smoker -0.030192 0.165026 -0.183 0.854833
SmokerStatusNever smoked -0.416898 0.222200 -1.876 0.060624 .
Med.Statin.LLDyes 0.020226 0.171804 0.118 0.906284
Med.all.antiplateletyes -0.121723 0.238580 -0.510 0.609914
GFR_MDRD 0.005240 0.003756 1.395 0.163024
BMI -0.002086 0.020132 -0.104 0.917473
MedHx_CVDyes -0.055130 0.148005 -0.372 0.709529
stenose50-70% 0.281200 0.883275 0.318 0.750211
stenose70-90% 0.529893 0.845581 0.627 0.530881
stenose90-99% 0.833372 0.847648 0.983 0.325530
stenose100% (Occlusion) 0.459584 1.110889 0.414 0.679088
stenose50-99% 14.334778 428.022224 0.033 0.973283
stenose70-99% 0.328315 1.165197 0.282 0.778122
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1260.0 on 1022 degrees of freedom
Residual deviance: 1204.9 on 1003 degrees of freedom
AIC: 1244.9
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.284484
Standard error............: 0.078214
Odds ratio (effect size)..: 0.752
Lower 95% CI..............: 0.645
Upper 95% CI..............: 0.877
Z-value...................: -3.637273
P-value...................: 0.0002755402
Hosmer and Lemeshow r^2...: 0.043715
Cox and Snell r^2.........: 0.052418
Nagelkerke's pseudo r^2...: 0.074016
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ ORdate_year + DiabetesStatus +
GFR_MDRD + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) ORdate_year DiabetesStatusDiabetes GFR_MDRD MedHx_CVDyes
-5.235e+02 2.619e-01 -4.535e-01 -9.264e-03 -3.696e-01
Degrees of Freedom: 497 Total (i.e. Null); 493 Residual
Null Deviance: 675.4
Residual Deviance: 656.8 AIC: 666.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7543 -1.2079 0.8134 1.0099 1.6284
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.303e+02 1.846e+02 -2.872 0.00408 **
currentDF[, PROTEIN] -9.703e-02 9.764e-02 -0.994 0.32037
Age 1.444e-03 1.244e-02 0.116 0.90761
Gendermale -6.259e-02 2.178e-01 -0.287 0.77380
ORdate_year 2.642e-01 9.214e-02 2.868 0.00414 **
Hypertension.compositeyes 3.948e-01 2.795e-01 1.413 0.15772
DiabetesStatusDiabetes -5.263e-01 2.397e-01 -2.196 0.02808 *
SmokerStatusEx-smoker -1.944e-01 2.133e-01 -0.911 0.36216
SmokerStatusNever smoked -9.473e-02 3.207e-01 -0.295 0.76768
Med.Statin.LLDyes -1.919e-01 2.227e-01 -0.861 0.38897
Med.all.antiplateletyes 2.845e-01 3.414e-01 0.833 0.40477
GFR_MDRD -9.088e-03 5.390e-03 -1.686 0.09180 .
BMI 1.066e-02 2.573e-02 0.414 0.67859
MedHx_CVDyes -3.417e-01 2.018e-01 -1.693 0.09049 .
stenose50-70% 1.416e+00 1.348e+00 1.050 0.29354
stenose70-90% 1.678e+00 1.259e+00 1.333 0.18247
stenose90-99% 1.412e+00 1.254e+00 1.126 0.26026
stenose100% (Occlusion) 1.621e+00 1.604e+00 1.011 0.31222
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 675.45 on 497 degrees of freedom
Residual deviance: 648.38 on 480 degrees of freedom
AIC: 684.38
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.097028
Standard error............: 0.097644
Odds ratio (effect size)..: 0.908
Lower 95% CI..............: 0.749
Upper 95% CI..............: 1.099
Z-value...................: -0.993689
P-value...................: 0.3203742
Hosmer and Lemeshow r^2...: 0.04007
Cox and Snell r^2.........: 0.052897
Nagelkerke's pseudo r^2...: 0.071252
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + SmokerStatus + Med.all.antiplatelet, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes
-800.8702 -0.5054 0.4003 -0.5831 -0.9310 0.7596
Degrees of Freedom: 495 Total (i.e. Null); 490 Residual
Null Deviance: 493.1
Residual Deviance: 447.1 AIC: 459.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3991 0.2993 0.4921 0.6733 1.3455
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.590e+02 8.436e+02 -0.900 0.368269
currentDF[, PROTEIN] -4.958e-01 1.301e-01 -3.812 0.000138 ***
Age -5.619e-03 1.617e-02 -0.347 0.728273
Gendermale -1.393e-01 2.861e-01 -0.487 0.626335
ORdate_year 3.866e-01 1.171e-01 3.302 0.000961 ***
Hypertension.compositeyes 2.724e-01 3.500e-01 0.778 0.436329
DiabetesStatusDiabetes 1.981e-01 3.234e-01 0.612 0.540222
SmokerStatusEx-smoker -5.865e-01 2.850e-01 -2.058 0.039624 *
SmokerStatusNever smoked -9.821e-01 3.912e-01 -2.510 0.012068 *
Med.Statin.LLDyes -9.019e-02 2.759e-01 -0.327 0.743778
Med.all.antiplateletyes 8.581e-01 4.047e-01 2.120 0.033973 *
GFR_MDRD -2.424e-03 7.057e-03 -0.344 0.731196
BMI -9.001e-03 3.494e-02 -0.258 0.796738
MedHx_CVDyes 7.111e-03 2.589e-01 0.027 0.978092
stenose50-70% -1.253e+01 8.103e+02 -0.015 0.987662
stenose70-90% -1.352e+01 8.103e+02 -0.017 0.986683
stenose90-99% -1.400e+01 8.103e+02 -0.017 0.986217
stenose100% (Occlusion) -1.323e+01 8.103e+02 -0.016 0.986975
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 493.05 on 495 degrees of freedom
Residual deviance: 439.04 on 478 degrees of freedom
AIC: 475.04
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.495808
Standard error............: 0.130055
Odds ratio (effect size)..: 0.609
Lower 95% CI..............: 0.472
Upper 95% CI..............: 0.786
Z-value...................: -3.812285
P-value...................: 0.0001376877
Hosmer and Lemeshow r^2...: 0.10955
Cox and Snell r^2.........: 0.103179
Nagelkerke's pseudo r^2...: 0.163795
Sample size of AE DB......: 2423
Sample size of model......: 496
Missing data %............: 79.52951
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked
0.8092 0.6602 0.6928 0.6592 -0.6083 0.1413
Degrees of Freedom: 497 Total (i.e. Null); 492 Residual
Null Deviance: 491.1
Residual Deviance: 444.9 AIC: 456.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5848 0.3017 0.4903 0.6709 1.8103
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.019e+02 8.644e+02 -0.349 0.7269
currentDF[, PROTEIN] 6.900e-01 1.329e-01 5.190 2.11e-07 ***
Age 2.784e-03 1.595e-02 0.175 0.8614
Gendermale 6.538e-01 2.651e-01 2.467 0.0136 *
ORdate_year 1.572e-01 1.183e-01 1.328 0.1841
Hypertension.compositeyes 6.489e-01 3.426e-01 1.894 0.0582 .
DiabetesStatusDiabetes -3.041e-01 2.996e-01 -1.015 0.3102
SmokerStatusEx-smoker -6.573e-01 2.800e-01 -2.347 0.0189 *
SmokerStatusNever smoked 4.369e-02 4.552e-01 0.096 0.9235
Med.Statin.LLDyes -2.220e-01 2.967e-01 -0.748 0.4545
Med.all.antiplateletyes 2.654e-01 4.143e-01 0.641 0.5218
GFR_MDRD 1.690e-03 7.138e-03 0.237 0.8128
BMI 3.527e-02 3.295e-02 1.070 0.2844
MedHx_CVDyes 1.223e-01 2.549e-01 0.480 0.6313
stenose50-70% -1.438e+01 8.312e+02 -0.017 0.9862
stenose70-90% -1.327e+01 8.312e+02 -0.016 0.9873
stenose90-99% -1.361e+01 8.312e+02 -0.016 0.9869
stenose100% (Occlusion) -1.294e+01 8.312e+02 -0.016 0.9876
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 491.11 on 497 degrees of freedom
Residual deviance: 435.50 on 480 degrees of freedom
AIC: 471.5
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.689959
Standard error............: 0.132948
Odds ratio (effect size)..: 1.994
Lower 95% CI..............: 1.536
Upper 95% CI..............: 2.587
Z-value...................: 5.1897
P-value...................: 2.106334e-07
Hosmer and Lemeshow r^2...: 0.113222
Cox and Snell r^2.........: 0.105647
Nagelkerke's pseudo r^2...: 0.168498
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes BMI MedHx_CVDyes
-1.99101 0.01776 0.74177 -0.50330 0.05039 0.34743
Degrees of Freedom: 497 Total (i.e. Null); 492 Residual
Null Deviance: 552.3
Residual Deviance: 530.6 AIC: 542.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0930 0.4579 0.6185 0.7655 1.4577
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -44.157516 210.353643 -0.210 0.83373
currentDF[, PROTEIN] 0.085258 0.112450 0.758 0.44834
Age 0.013062 0.014168 0.922 0.35654
Gendermale 0.764000 0.235270 3.247 0.00116 **
ORdate_year 0.020756 0.104972 0.198 0.84326
Hypertension.compositeyes 0.238795 0.310574 0.769 0.44196
DiabetesStatusDiabetes -0.517784 0.264433 -1.958 0.05022 .
SmokerStatusEx-smoker -0.079166 0.246460 -0.321 0.74805
SmokerStatusNever smoked 0.053141 0.367384 0.145 0.88499
Med.Statin.LLDyes -0.087080 0.260840 -0.334 0.73850
Med.all.antiplateletyes -0.106642 0.399317 -0.267 0.78942
GFR_MDRD -0.005579 0.006259 -0.891 0.37278
BMI 0.050000 0.029351 1.704 0.08847 .
MedHx_CVDyes 0.344835 0.224565 1.536 0.12464
stenose50-70% 1.271129 1.377715 0.923 0.35620
stenose70-90% 1.170764 1.265627 0.925 0.35494
stenose90-99% 1.367189 1.262889 1.083 0.27899
stenose100% (Occlusion) 1.478726 1.723795 0.858 0.39099
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 552.26 on 497 degrees of freedom
Residual deviance: 526.41 on 480 degrees of freedom
AIC: 562.41
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.085258
Standard error............: 0.11245
Odds ratio (effect size)..: 1.089
Lower 95% CI..............: 0.874
Upper 95% CI..............: 1.358
Z-value...................: 0.758181
P-value...................: 0.4483429
Hosmer and Lemeshow r^2...: 0.046812
Cox and Snell r^2.........: 0.050589
Nagelkerke's pseudo r^2...: 0.075495
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + Med.Statin.LLD + GFR_MDRD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
-762.20937 0.39750 0.32386 0.38053 0.51246 -0.00836
Degrees of Freedom: 493 Total (i.e. Null); 488 Residual
Null Deviance: 671.2
Residual Deviance: 630.4 AIC: 642.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0233 -1.1555 0.7355 0.9909 1.5585
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.459e+02 5.422e+02 -1.376 0.168912
currentDF[, PROTEIN] 3.810e-01 1.016e-01 3.748 0.000178 ***
Age -1.768e-02 1.269e-02 -1.393 0.163596
Gendermale 3.458e-01 2.195e-01 1.575 0.115240
ORdate_year 3.798e-01 9.574e-02 3.967 7.28e-05 ***
Hypertension.compositeyes 5.242e-02 2.887e-01 0.182 0.855941
DiabetesStatusDiabetes -1.422e-01 2.464e-01 -0.577 0.563990
SmokerStatusEx-smoker 5.878e-02 2.183e-01 0.269 0.787743
SmokerStatusNever smoked 1.942e-01 3.266e-01 0.595 0.552050
Med.Statin.LLDyes 4.288e-01 2.239e-01 1.915 0.055488 .
Med.all.antiplateletyes -1.192e-01 3.515e-01 -0.339 0.734579
GFR_MDRD -9.966e-03 5.527e-03 -1.803 0.071365 .
BMI -3.345e-03 2.565e-02 -0.130 0.896243
MedHx_CVDyes 1.279e-01 2.039e-01 0.627 0.530510
stenose50-70% -1.355e+01 5.071e+02 -0.027 0.978679
stenose70-90% -1.325e+01 5.071e+02 -0.026 0.979153
stenose90-99% -1.355e+01 5.071e+02 -0.027 0.978687
stenose100% (Occlusion) -1.393e+01 5.071e+02 -0.027 0.978091
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 671.15 on 493 degrees of freedom
Residual deviance: 623.39 on 476 degrees of freedom
AIC: 659.39
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.380999
Standard error............: 0.101645
Odds ratio (effect size)..: 1.464
Lower 95% CI..............: 1.199
Upper 95% CI..............: 1.786
Z-value...................: 3.748326
P-value...................: 0.0001780186
Hosmer and Lemeshow r^2...: 0.071171
Cox and Snell r^2.........: 0.092166
Nagelkerke's pseudo r^2...: 0.124048
Sample size of AE DB......: 2423
Sample size of model......: 494
Missing data %............: 79.61205
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
3.58159 -0.46804 -0.03057 -0.73346
Degrees of Freedom: 495 Total (i.e. Null); 492 Residual
Null Deviance: 595.8
Residual Deviance: 558.3 AIC: 566.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3253 -1.1830 0.6182 0.8468 1.4395
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.655e+02 5.405e+02 -0.491 0.62331
currentDF[, PROTEIN] -4.611e-01 1.126e-01 -4.094 4.23e-05 ***
Age -3.691e-02 1.427e-02 -2.587 0.00969 **
Gendermale -8.251e-01 2.669e-01 -3.091 0.00199 **
ORdate_year 1.421e-01 1.019e-01 1.395 0.16311
Hypertension.compositeyes -3.209e-01 3.348e-01 -0.959 0.33780
DiabetesStatusDiabetes -2.069e-01 2.620e-01 -0.790 0.42963
SmokerStatusEx-smoker 2.049e-01 2.393e-01 0.856 0.39174
SmokerStatusNever smoked -1.393e-01 3.395e-01 -0.410 0.68158
Med.Statin.LLDyes -1.806e-01 2.464e-01 -0.733 0.46353
Med.all.antiplateletyes -8.168e-02 3.820e-01 -0.214 0.83070
GFR_MDRD -1.716e-03 5.923e-03 -0.290 0.77209
BMI -2.059e-02 2.927e-02 -0.703 0.48175
MedHx_CVDyes -1.088e-01 2.247e-01 -0.484 0.62815
stenose50-70% -1.382e+01 5.005e+02 -0.028 0.97797
stenose70-90% -1.404e+01 5.005e+02 -0.028 0.97762
stenose90-99% -1.391e+01 5.005e+02 -0.028 0.97782
stenose100% (Occlusion) -1.486e+01 5.005e+02 -0.030 0.97631
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 595.82 on 495 degrees of freedom
Residual deviance: 547.99 on 478 degrees of freedom
AIC: 583.99
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.461087
Standard error............: 0.112615
Odds ratio (effect size)..: 0.631
Lower 95% CI..............: 0.506
Upper 95% CI..............: 0.786
Z-value...................: -4.094379
P-value...................: 4.233022e-05
Hosmer and Lemeshow r^2...: 0.080275
Cox and Snell r^2.........: 0.091928
Nagelkerke's pseudo r^2...: 0.131479
Sample size of AE DB......: 2423
Sample size of model......: 496
Missing data %............: 79.52951 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
B. Cross-sectional analysis symptoms
We will perform a cross-sectional analysis between plaque MCP1 levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:
- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic
Model 1
In this model we correct for Age, Gender, and year of surgery.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ml_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
-124.83943 0.27031 0.02877 -0.51749 0.06249
Degrees of Freedom: 1198 Total (i.e. Null); 1194 Residual
Null Deviance: 827.2
Residual Deviance: 797.3 AIC: 807.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5524 0.3495 0.4339 0.5243 0.8965
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -124.83943 68.66012 -1.818 0.06903 .
currentDF[, PROTEIN] 0.27031 0.10237 2.641 0.00828 **
Age 0.02877 0.01023 2.811 0.00493 **
Gendermale -0.51749 0.22116 -2.340 0.01929 *
ORdate_year 0.06249 0.03424 1.825 0.06796 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 827.22 on 1198 degrees of freedom
Residual deviance: 797.31 on 1194 degrees of freedom
AIC: 807.31
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.270307
Standard error............: 0.102367
Odds ratio (effect size)..: 1.31
Lower 95% CI..............: 1.072
Upper 95% CI..............: 1.602
Z-value...................: 2.640561
P-value...................: 0.008276887
Hosmer and Lemeshow r^2...: 0.036146
Cox and Snell r^2.........: 0.02463
Nagelkerke's pseudo r^2...: 0.049419
Sample size of AE DB......: 2423
Sample size of model......: 1199
Missing data %............: 50.51589
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year
-473.3895 0.3371 0.2371
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 479
Residual Deviance: 468.7 AIC: 474.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3911 0.4414 0.5340 0.6219 1.0452
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -480.62215 225.42835 -2.132 0.03300 *
currentDF[, PROTEIN] 0.36235 0.12346 2.935 0.00334 **
Age 0.01562 0.01370 1.140 0.25440
Gendermale -0.29174 0.27407 -1.064 0.28711
ORdate_year 0.24030 0.11253 2.135 0.03272 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 478.98 on 555 degrees of freedom
Residual deviance: 466.39 on 551 degrees of freedom
AIC: 476.39
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.362354
Standard error............: 0.123463
Odds ratio (effect size)..: 1.437
Lower 95% CI..............: 1.128
Upper 95% CI..............: 1.83
Z-value...................: 2.934919
P-value...................: 0.003336347
Hosmer and Lemeshow r^2...: 0.026279
Cox and Snell r^2.........: 0.022385
Nagelkerke's pseudo r^2...: 0.038764
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ml_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Med.all.antiplatelet + stenose,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year Med.all.antiplateletyes
-145.03274 0.32511 0.02147 -0.48726 0.08040 -0.91400
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-13.07895 -14.66964 -14.29506 0.03649 -15.84146 -0.73998
Degrees of Freedom: 1037 Total (i.e. Null); 1026 Residual
Null Deviance: 726.9
Residual Deviance: 679.9 AIC: 703.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.2614 0.2818 0.4201 0.5408 1.0245
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.486e+02 9.514e+02 -0.156 0.87591
currentDF[, PROTEIN] 3.063e-01 1.122e-01 2.729 0.00636 **
Age 2.888e-02 1.278e-02 2.260 0.02385 *
Gendermale -4.336e-01 2.409e-01 -1.800 0.07187 .
ORdate_year 8.214e-02 3.946e-02 2.082 0.03739 *
Hypertension.compositeyes -3.362e-01 3.458e-01 -0.972 0.33083
DiabetesStatusDiabetes -4.766e-02 2.441e-01 -0.195 0.84521
SmokerStatusEx-smoker -3.345e-01 2.345e-01 -1.426 0.15373
SmokerStatusNever smoked -2.811e-03 3.574e-01 -0.008 0.99372
Med.Statin.LLDyes -2.461e-01 2.688e-01 -0.916 0.35983
Med.all.antiplateletyes -9.270e-01 4.806e-01 -1.929 0.05372 .
GFR_MDRD 6.238e-03 5.532e-03 1.128 0.25950
BMI -8.706e-03 2.805e-02 -0.310 0.75628
MedHx_CVDyes 9.157e-02 2.110e-01 0.434 0.66436
stenose50-70% -1.317e+01 9.481e+02 -0.014 0.98891
stenose70-90% -1.473e+01 9.481e+02 -0.016 0.98760
stenose90-99% -1.437e+01 9.481e+02 -0.015 0.98791
stenose100% (Occlusion) -1.476e-01 1.228e+03 0.000 0.99990
stenose50-99% -1.613e+01 9.481e+02 -0.017 0.98642
stenose70-99% -7.880e-01 1.183e+03 -0.001 0.99947
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 726.94 on 1037 degrees of freedom
Residual deviance: 673.02 on 1018 degrees of freedom
AIC: 713.02
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.306293
Standard error............: 0.112242
Odds ratio (effect size)..: 1.358
Lower 95% CI..............: 1.09
Upper 95% CI..............: 1.693
Z-value...................: 2.728861
P-value...................: 0.006355351
Hosmer and Lemeshow r^2...: 0.074179
Cox and Snell r^2.........: 0.050624
Nagelkerke's pseudo r^2...: 0.100528
Sample size of AE DB......: 2423
Sample size of model......: 1038
Missing data %............: 57.16054
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + Med.Statin.LLD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year Med.Statin.LLDyes
-529.0018 0.3015 0.2650 -0.4436
Degrees of Freedom: 497 Total (i.e. Null); 494 Residual
Null Deviance: 442.3
Residual Deviance: 431.4 AIC: 439.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4505 0.3657 0.5162 0.6508 1.2570
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.467e+02 1.385e+03 -0.395 0.69306
currentDF[, PROTEIN] 3.487e-01 1.293e-01 2.697 0.00701 **
Age 1.924e-02 1.635e-02 1.177 0.23928
Gendermale -3.424e-01 2.977e-01 -1.150 0.25001
ORdate_year 2.808e-01 1.220e-01 2.301 0.02139 *
Hypertension.compositeyes -5.366e-01 4.410e-01 -1.217 0.22366
DiabetesStatusDiabetes 1.954e-01 3.251e-01 0.601 0.54780
SmokerStatusEx-smoker -1.745e-01 2.861e-01 -0.610 0.54197
SmokerStatusNever smoked -4.281e-01 4.090e-01 -1.047 0.29516
Med.Statin.LLDyes -3.682e-01 3.131e-01 -1.176 0.23961
Med.all.antiplateletyes -4.933e-01 5.140e-01 -0.960 0.33722
GFR_MDRD 9.396e-03 7.081e-03 1.327 0.18455
BMI 1.197e-02 3.425e-02 0.349 0.72676
MedHx_CVDyes 8.287e-02 2.647e-01 0.313 0.75424
stenose50-70% -1.392e+01 1.363e+03 -0.010 0.99185
stenose70-90% -1.531e+01 1.363e+03 -0.011 0.99104
stenose90-99% -1.494e+01 1.363e+03 -0.011 0.99126
stenose100% (Occlusion) -8.723e-02 1.712e+03 0.000 0.99996
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 442.26 on 497 degrees of freedom
Residual deviance: 417.29 on 480 degrees of freedom
AIC: 453.29
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.348715
Standard error............: 0.129319
Odds ratio (effect size)..: 1.417
Lower 95% CI..............: 1.1
Upper 95% CI..............: 1.826
Z-value...................: 2.696545
P-value...................: 0.007006285
Hosmer and Lemeshow r^2...: 0.056471
Cox and Snell r^2.........: 0.048913
Nagelkerke's pseudo r^2...: 0.083108
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
C. Longitudinal analysis secondary clinical outcome
For the longitudinal analyses of plaque MCP1 levels and secondary cardiovascular events over a three-year follow-up period.
The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..
The secondary outcomes will be
- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable:
epstroke.3years, these include:- cerebral infarction (CVA/stroke)
- cerebral bleeding (CVA/stroke)
- fatal cerebral infarction
- fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable:
epcoronary.3years, these include:- myocardial infarction (MI)
- coronary angioplasty (PCI/PTCA)
- cardiovascular death (exact cause to be investigated)
- coronary bypass (CABG)
- fatal myocardial infarction (MI)
- sudden death.
- cardiovascular death - variable:
epcvdeath.3years, these include:- cardiovascular death (exact cause to be investigated)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..
30- and 90-days FU events
We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90
# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
AEDB.CEA <- AEDB.CEA %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:
- MACE (
epmajor.3years) - Stroke (
epstroke.3years) - Coronary events (
epcoronary.3years) - Cardiovascular death (
epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
AEDB.CEA <- AEDB.CEA %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB.CEA)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Sanity checks
First we do some sanity checks and inventory the time-to-event and event variables.
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")Loading required package: survivalinstall.packages.auto("survminer")Loading required package: survminerinstall.packages.auto("Hmisc")Loading required package: Hmisc
Loading required package: lattice
Loading required package: Formula
Attaching package: ‘Hmisc’
The following objects are masked from ‘package:dplyr’:
src, summarize
The following objects are masked from ‘package:base’:
format.pval, unitscat("* Creating function to summarize Cox regression and prepare container for results.")* Creating function to summarize Cox regression and prepare container for results.# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
if (nrow(summary(coxfit)$coefficients) == 1) {
output = c(protein, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
cox.sum <- summary(coxfit)
cox.effectsize = cox.sum$coefficients[1,1]
cox.SE = cox.sum$coefficients[1,3]
cox.HReffect = cox.sum$coefficients[1,2]
cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
cox.zvalue = cox.sum$coefficients[1,4]
cox.pvalue = cox.sum$coefficients[1,5]
cox.sample_size = cox.sum$n
cox.nevents = cox.sum$nevent
output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
cat("We have collected the following:\n")
cat("Dataset used..............:", DATASET, "\n")
cat("Outcome analyzed..........:", OUTCOME, "\n")
cat("Protein...................:", protein, "\n")
cat("Effect size...............:", round(cox.effectsize, 6), "\n")
cat("Standard error............:", round(cox.SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
cat("T-value...................:", round(cox.zvalue, 6), "\n")
cat("P-value...................:", signif(cox.pvalue, 8), "\n")
cat("Sample size in model......:", cox.sample_size, "\n")
cat("Number of events..........:", cox.nevents, "\n")
}
return(output)
print(output)
}
times = c("ep_major_t_3years",
"ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")
endpoints = c("epmajor.3years",
"epstroke.3years", "epcoronary.3years", "epcvdeath.3years")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints){
require(labelled)
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.3years"
0 1
2035 265
[1] "Printing the summary of: epstroke.3years"
0 1
2171 130
[1] "Printing the summary of: epcoronary.3years"
0 1
2119 182
[1] "Printing the summary of: epcvdeath.3years"
0 1
2210 90 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.710 3.000 2.573 3.000 3.000 125
[1] "Printing the summary of: ep_stroke_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.877 3.000 2.624 3.000 3.000 125
[1] "Printing the summary of: ep_coronary_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.784 3.000 2.622 3.000 3.000 125
[1] "Printing the summary of: ep_cvdeath_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00274 2.91233 3.00000 2.70902 3.00000 3.00000 125 for (eventtime in times){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"
times30 = c("ep_major_t_30days",
"ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")
endpoints30 = c("epmajor.30days",
"epstroke.30days", "epcoronary.30days", "epcvdeath.30days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints30){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.30days"
0 1
2222 78
[1] "Printing the summary of: epstroke.30days"
0 1
2248 53
[1] "Printing the summary of: epcoronary.30days"
0 1
2267 34
[1] "Printing the summary of: epcvdeath.30days"
0 1
2288 12 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times30){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 30.00 30.00 29.09 30.00 30.00 125
[1] "Printing the summary of: ep_stroke_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 30.00 30.00 29.32 30.00 30.00 125
[1] "Printing the summary of: ep_coronary_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 30.00 30.00 29.54 30.00 30.00 125
[1] "Printing the summary of: ep_cvdeath_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.001 30.000 30.000 29.854 30.000 30.000 125 for (eventtime in times30){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_30days"
[1] "Printing the distribution of: ep_stroke_t_30days"
[1] "Printing the distribution of: ep_coronary_t_30days"
[1] "Printing the distribution of: ep_cvdeath_t_30days"
times90 = c("ep_major_t_90days",
"ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")
endpoints90 = c("epmajor.90days",
"epstroke.90days", "epcoronary.90days", "epcvdeath.90days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints90){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.90days"
0 1
2206 94
[1] "Printing the summary of: epstroke.90days"
0 1
2241 60
[1] "Printing the summary of: epcoronary.90days"
0 1
2257 44
[1] "Printing the summary of: epcvdeath.90days"
0 1
2281 19 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times90){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 90.00 90.00 86.75 90.00 90.00 125
[1] "Printing the summary of: ep_stroke_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 90.00 90.00 87.51 90.00 90.00 125
[1] "Printing the summary of: ep_coronary_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 90.00 90.00 88.21 90.00 90.00 125
[1] "Printing the summary of: ep_cvdeath_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.001 90.000 90.000 89.320 90.000 90.000 125 for (eventtime in times90){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_90days"
[1] "Printing the distribution of: ep_stroke_t_90days"
[1] "Printing the distribution of: ep_coronary_t_90days"
[1] "Printing the distribution of: ep_cvdeath_t_90days"
Cox regressions
Let’s perform the actual Cox-regressions. We will apply a couple of models:
- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
3 years follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 140
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 0.01185 1.01192 0.18373 0.064 0.948575
Age 0.03489 1.03550 0.01003 3.478 0.000506 ***
Gendermale 0.35203 1.42196 0.20065 1.754 0.079351 .
ORdate_year -0.02361 0.97667 0.03018 -0.782 0.434149
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.0119 0.9882 0.7059 1.451
Age 1.0355 0.9657 1.0153 1.056
Gendermale 1.4220 0.7033 0.9596 2.107
ORdate_year 0.9767 1.0239 0.9206 1.036
Concordance= 0.589 (se = 0.025 )
Likelihood ratio test= 16.08 on 4 df, p=0.003
Wald test = 15.15 on 4 df, p=0.004
Score (logrank) test = 15.23 on 4 df, p=0.004
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.01185
Standard error............: 0.18373
Odds ratio (effect size)..: 1.012
Lower 95% CI..............: 0.706
Upper 95% CI..............: 1.451
T-value...................: 0.064496
P-value...................: 0.9485755
Sample size in model......: 1187
Number of events..........: 140
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 70
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.22427 0.79910 0.24647 -0.910 0.3629
Age 0.02639 1.02674 0.01475 1.789 0.0736 .
Gendermale 0.87183 2.39128 0.34246 2.546 0.0109 *
ORdate_year -0.03519 0.96542 0.11300 -0.311 0.7555
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.7991 1.2514 0.4929 1.295
Age 1.0267 0.9740 0.9975 1.057
Gendermale 2.3913 0.4182 1.2222 4.679
ORdate_year 0.9654 1.0358 0.7736 1.205
Concordance= 0.618 (se = 0.034 )
Likelihood ratio test= 12.21 on 4 df, p=0.02
Wald test = 10.74 on 4 df, p=0.03
Score (logrank) test = 11.16 on 4 df, p=0.02
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.224272
Standard error............: 0.246475
Odds ratio (effect size)..: 0.799
Lower 95% CI..............: 0.493
Upper 95% CI..............: 1.295
T-value...................: -0.909918
P-value...................: 0.3628658
Sample size in model......: 549
Number of events..........: 70
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 74
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 0.05159 1.05295 0.25152 0.205 0.83747
Age 0.03709 1.03779 0.01382 2.684 0.00728 **
Gendermale 0.09193 1.09629 0.26020 0.353 0.72385
ORdate_year -0.04704 0.95405 0.04159 -1.131 0.25806
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.053 0.9497 0.6432 1.724
Age 1.038 0.9636 1.0101 1.066
Gendermale 1.096 0.9122 0.6583 1.826
ORdate_year 0.954 1.0482 0.8794 1.035
Concordance= 0.591 (se = 0.035 )
Likelihood ratio test= 8.33 on 4 df, p=0.08
Wald test = 7.9 on 4 df, p=0.1
Score (logrank) test = 7.96 on 4 df, p=0.09
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.051594
Standard error............: 0.251515
Odds ratio (effect size)..: 1.053
Lower 95% CI..............: 0.643
Upper 95% CI..............: 1.724
T-value...................: 0.205134
P-value...................: 0.8374673
Sample size in model......: 1187
Number of events..........: 74
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 36
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.366525 0.693139 0.345901 -1.060 0.289
Age 0.007526 1.007554 0.019822 0.380 0.704
Gendermale 0.332937 1.395059 0.403044 0.826 0.409
ORdate_year -0.014799 0.985310 0.157445 -0.094 0.925
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.6931 1.4427 0.3519 1.365
Age 1.0076 0.9925 0.9692 1.047
Gendermale 1.3951 0.7168 0.6332 3.074
ORdate_year 0.9853 1.0149 0.7237 1.341
Concordance= 0.571 (se = 0.043 )
Likelihood ratio test= 1.96 on 4 df, p=0.7
Wald test = 1.92 on 4 df, p=0.8
Score (logrank) test = 1.93 on 4 df, p=0.7
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.366525
Standard error............: 0.345901
Odds ratio (effect size)..: 0.693
Lower 95% CI..............: 0.352
Upper 95% CI..............: 1.365
T-value...................: -1.059623
P-value...................: 0.2893161
Sample size in model......: 549
Number of events..........: 36
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 91
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 0.172342 1.188084 0.225648 0.764 0.4450
Age 0.008689 1.008727 0.012048 0.721 0.4708
Gendermale 0.643664 1.903442 0.270491 2.380 0.0173 *
ORdate_year -0.055903 0.945631 0.037238 -1.501 0.1333
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.1881 0.8417 0.7634 1.849
Age 1.0087 0.9913 0.9852 1.033
Gendermale 1.9034 0.5254 1.1202 3.234
ORdate_year 0.9456 1.0575 0.8791 1.017
Concordance= 0.591 (se = 0.031 )
Likelihood ratio test= 9.57 on 4 df, p=0.05
Wald test = 8.73 on 4 df, p=0.07
Score (logrank) test = 8.95 on 4 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.172342
Standard error............: 0.225648
Odds ratio (effect size)..: 1.188
Lower 95% CI..............: 0.763
Upper 95% CI..............: 1.849
T-value...................: 0.763763
P-value...................: 0.4450084
Sample size in model......: 1187
Number of events..........: 91
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 46
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.24448 1.27695 0.30911 0.791 0.4290
Age 0.03668 1.03736 0.01872 1.959 0.0501 .
Gendermale 0.92420 2.51986 0.43913 2.105 0.0353 *
ORdate_year -0.23892 0.78748 0.13604 -1.756 0.0790 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 1.2770 0.7831 0.6967 2.340
Age 1.0374 0.9640 1.0000 1.076
Gendermale 2.5199 0.3968 1.0656 5.959
ORdate_year 0.7875 1.2699 0.6032 1.028
Concordance= 0.652 (se = 0.039 )
Likelihood ratio test= 13.98 on 4 df, p=0.007
Wald test = 12.67 on 4 df, p=0.01
Score (logrank) test = 13.17 on 4 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.244478
Standard error............: 0.309109
Odds ratio (effect size)..: 1.277
Lower 95% CI..............: 0.697
Upper 95% CI..............: 2.34
T-value...................: 0.79091
P-value...................: 0.4289965
Sample size in model......: 549
Number of events..........: 46
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 45
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] -0.11518 0.89120 0.32234 -0.357 0.7208
Age 0.09047 1.09469 0.02008 4.505 6.63e-06 ***
Gendermale 0.91435 2.49514 0.41402 2.208 0.0272 *
ORdate_year -0.06875 0.93356 0.05424 -1.267 0.2050
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 0.8912 1.1221 0.4738 1.676
Age 1.0947 0.9135 1.0524 1.139
Gendermale 2.4951 0.4008 1.1084 5.617
ORdate_year 0.9336 1.0712 0.8394 1.038
Concordance= 0.716 (se = 0.039 )
Likelihood ratio test= 29.09 on 4 df, p=7e-06
Wald test = 24.68 on 4 df, p=6e-05
Score (logrank) test = 25.41 on 4 df, p=4e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: -0.115182
Standard error............: 0.322341
Odds ratio (effect size)..: 0.891
Lower 95% CI..............: 0.474
Upper 95% CI..............: 1.676
T-value...................: -0.357328
P-value...................: 0.7208462
Sample size in model......: 1187
Number of events..........: 45
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 26
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.03367 0.96689 0.40417 -0.083 0.9336
Age 0.05571 1.05729 0.02549 2.185 0.0289 *
Gendermale 1.05290 2.86594 0.61477 1.713 0.0868 .
ORdate_year -0.11039 0.89548 0.18082 -0.611 0.5415
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.9669 1.0342 0.4379 2.135
Age 1.0573 0.9458 1.0058 1.111
Gendermale 2.8659 0.3489 0.8590 9.562
ORdate_year 0.8955 1.1167 0.6283 1.276
Concordance= 0.679 (se = 0.06 )
Likelihood ratio test= 9.52 on 4 df, p=0.05
Wald test = 8.25 on 4 df, p=0.08
Score (logrank) test = 8.62 on 4 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.03367
Standard error............: 0.40417
Odds ratio (effect size)..: 0.967
Lower 95% CI..............: 0.438
Upper 95% CI..............: 2.135
T-value...................: -0.083308
P-value...................: 0.9336068
Sample size in model......: 549
Number of events..........: 26 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 115
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.291e-01 1.138e+00 2.002e-01 0.645 0.518966
Age 3.304e-02 1.034e+00 1.293e-02 2.556 0.010591 *
Gendermale 3.709e-01 1.449e+00 2.288e-01 1.621 0.105078
ORdate_year -1.222e-02 9.879e-01 3.470e-02 -0.352 0.724616
Hypertension.compositeno -4.257e-01 6.533e-01 3.572e-01 -1.192 0.233306
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.766e-02 9.825e-01 2.237e-01 -0.079 0.937094
SmokerStatusEx-smoker -5.003e-01 6.063e-01 2.096e-01 -2.387 0.016973 *
SmokerStatusNever smoked -8.121e-01 4.439e-01 3.418e-01 -2.376 0.017500 *
Med.Statin.LLDno 2.512e-01 1.286e+00 2.183e-01 1.151 0.249766
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.271e-01 1.533e+00 2.637e-01 1.620 0.105327
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.926e-02 9.809e-01 4.962e-03 -3.880 0.000104 ***
BMI 5.407e-02 1.056e+00 2.610e-02 2.071 0.038324 *
MedHx_CVDyes 5.365e-01 1.710e+00 2.221e-01 2.416 0.015694 *
stenose0-49% -1.571e+01 1.504e-07 2.447e+03 -0.006 0.994877
stenose50-70% -8.674e-01 4.200e-01 8.780e-01 -0.988 0.323168
stenose70-90% -3.100e-01 7.334e-01 7.471e-01 -0.415 0.678201
stenose90-99% -2.933e-01 7.458e-01 7.560e-01 -0.388 0.698046
stenose100% (Occlusion) -1.521e-01 8.589e-01 1.253e+00 -0.121 0.903378
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.531e+01 2.252e-07 2.926e+03 -0.005 0.995826
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.138e+00 8.789e-01 0.76852 1.6846
Age 1.034e+00 9.675e-01 1.00773 1.0601
Gendermale 1.449e+00 6.901e-01 0.92531 2.2690
ORdate_year 9.879e-01 1.012e+00 0.92291 1.0574
Hypertension.compositeno 6.533e-01 1.531e+00 0.32438 1.3157
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.825e-01 1.018e+00 0.63374 1.5232
SmokerStatusEx-smoker 6.063e-01 1.649e+00 0.40210 0.9143
SmokerStatusNever smoked 4.439e-01 2.253e+00 0.22718 0.8674
Med.Statin.LLDno 1.286e+00 7.779e-01 0.83813 1.9719
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.533e+00 6.524e-01 0.91414 2.5702
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.809e-01 1.019e+00 0.97143 0.9905
BMI 1.056e+00 9.474e-01 1.00291 1.1110
MedHx_CVDyes 1.710e+00 5.848e-01 1.10656 2.6424
stenose0-49% 1.504e-07 6.648e+06 0.00000 Inf
stenose50-70% 4.200e-01 2.381e+00 0.07515 2.3477
stenose70-90% 7.334e-01 1.363e+00 0.16959 3.1720
stenose90-99% 7.458e-01 1.341e+00 0.16947 3.2821
stenose100% (Occlusion) 8.589e-01 1.164e+00 0.07373 10.0065
stenoseNA NA NA NA NA
stenose50-99% 2.252e-07 4.441e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.698 (se = 0.023 )
Likelihood ratio test= 63.82 on 19 df, p=9e-07
Wald test = 58.85 on 19 df, p=6e-06
Score (logrank) test = 62.26 on 19 df, p=2e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.129126
Standard error............: 0.200214
Odds ratio (effect size)..: 1.138
Lower 95% CI..............: 0.769
Upper 95% CI..............: 1.685
T-value...................: 0.64494
P-value...................: 0.5189661
Sample size in model......: 1029
Number of events..........: 115
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 61
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -3.920e-01 6.757e-01 2.685e-01 -1.460 0.1443
Age 3.117e-02 1.032e+00 1.813e-02 1.719 0.0857 .
Gendermale 8.127e-01 2.254e+00 3.652e-01 2.226 0.0260 *
ORdate_year -3.205e-02 9.685e-01 1.249e-01 -0.257 0.7975
Hypertension.compositeno -7.542e-01 4.704e-01 5.309e-01 -1.421 0.1555
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.309e-01 1.879e+00 2.951e-01 2.138 0.0325 *
SmokerStatusEx-smoker -6.391e-01 5.278e-01 2.880e-01 -2.219 0.0265 *
SmokerStatusNever smoked -3.224e-01 7.244e-01 4.307e-01 -0.748 0.4542
Med.Statin.LLDno 2.275e-01 1.255e+00 2.962e-01 0.768 0.4426
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -1.296e-02 9.871e-01 4.530e-01 -0.029 0.9772
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.091e-02 9.891e-01 6.796e-03 -1.606 0.1083
BMI 5.177e-03 1.005e+00 3.447e-02 0.150 0.8806
MedHx_CVDyes 5.574e-01 1.746e+00 3.075e-01 1.813 0.0699 .
stenose0-49% -1.651e+01 6.744e-08 3.444e+03 -0.005 0.9962
stenose50-70% -1.690e+00 1.845e-01 1.448e+00 -1.167 0.2431
stenose70-90% -7.523e-01 4.713e-01 1.049e+00 -0.717 0.4731
stenose90-99% -1.050e+00 3.501e-01 1.055e+00 -0.995 0.3198
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 6.757e-01 1.480e+00 0.39920 1.144
Age 1.032e+00 9.693e-01 0.99563 1.069
Gendermale 2.254e+00 4.436e-01 1.10189 4.611
ORdate_year 9.685e-01 1.033e+00 0.75814 1.237
Hypertension.compositeno 4.704e-01 2.126e+00 0.16617 1.332
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.879e+00 5.321e-01 1.05394 3.351
SmokerStatusEx-smoker 5.278e-01 1.895e+00 0.30015 0.928
SmokerStatusNever smoked 7.244e-01 1.380e+00 0.31142 1.685
Med.Statin.LLDno 1.255e+00 7.965e-01 0.70247 2.244
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.871e-01 1.013e+00 0.40623 2.399
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.891e-01 1.011e+00 0.97606 1.002
BMI 1.005e+00 9.948e-01 0.93952 1.075
MedHx_CVDyes 1.746e+00 5.727e-01 0.95573 3.190
stenose0-49% 6.744e-08 1.483e+07 0.00000 Inf
stenose50-70% 1.845e-01 5.420e+00 0.01080 3.151
stenose70-90% 4.713e-01 2.122e+00 0.06036 3.679
stenose90-99% 3.501e-01 2.857e+00 0.04426 2.768
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.703 (se = 0.03 )
Likelihood ratio test= 32.87 on 17 df, p=0.01
Wald test = 29.3 on 17 df, p=0.03
Score (logrank) test = 31.16 on 17 df, p=0.02
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.392011
Standard error............: 0.268514
Odds ratio (effect size)..: 0.676
Lower 95% CI..............: 0.399
Upper 95% CI..............: 1.144
T-value...................: -1.459925
P-value...................: 0.1443106
Sample size in model......: 493
Number of events..........: 61
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 59
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.594e-01 1.173e+00 2.824e-01 0.564 0.5724
Age 4.416e-02 1.045e+00 1.793e-02 2.462 0.0138 *
Gendermale -4.998e-02 9.513e-01 3.010e-01 -0.166 0.8681
ORdate_year -3.475e-02 9.658e-01 4.903e-02 -0.709 0.4785
Hypertension.compositeno -1.230e-03 9.988e-01 4.192e-01 -0.003 0.9977
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -2.173e-02 9.785e-01 3.172e-01 -0.068 0.9454
SmokerStatusEx-smoker -1.136e-01 8.926e-01 2.965e-01 -0.383 0.7015
SmokerStatusNever smoked -9.518e-01 3.860e-01 5.240e-01 -1.817 0.0693 .
Med.Statin.LLDno 3.482e-01 1.417e+00 2.971e-01 1.172 0.2412
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.779e-01 1.459e+00 3.721e-01 1.016 0.3098
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.561e-03 9.964e-01 7.027e-03 -0.507 0.6124
BMI 8.022e-02 1.084e+00 3.436e-02 2.335 0.0195 *
MedHx_CVDyes 3.650e-01 1.441e+00 2.941e-01 1.241 0.2146
stenose0-49% -1.549e+01 1.867e-07 3.367e+03 -0.005 0.9963
stenose50-70% -6.477e-01 5.233e-01 1.173e+00 -0.552 0.5810
stenose70-90% -4.535e-01 6.354e-01 1.055e+00 -0.430 0.6673
stenose90-99% -5.009e-01 6.060e-01 1.070e+00 -0.468 0.6396
stenose100% (Occlusion) 3.518e-01 1.422e+00 1.459e+00 0.241 0.8094
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.518e+01 2.547e-07 3.975e+03 -0.004 0.9970
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.173e+00 8.526e-01 0.67425 2.040
Age 1.045e+00 9.568e-01 1.00905 1.083
Gendermale 9.513e-01 1.051e+00 0.52730 1.716
ORdate_year 9.658e-01 1.035e+00 0.87735 1.063
Hypertension.compositeno 9.988e-01 1.001e+00 0.43921 2.271
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.785e-01 1.022e+00 0.52549 1.822
SmokerStatusEx-smoker 8.926e-01 1.120e+00 0.49923 1.596
SmokerStatusNever smoked 3.860e-01 2.590e+00 0.13823 1.078
Med.Statin.LLDno 1.417e+00 7.059e-01 0.79124 2.536
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.459e+00 6.853e-01 0.70369 3.026
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.964e-01 1.004e+00 0.98282 1.010
BMI 1.084e+00 9.229e-01 1.01297 1.159
MedHx_CVDyes 1.441e+00 6.942e-01 0.80943 2.564
stenose0-49% 1.867e-07 5.356e+06 0.00000 Inf
stenose50-70% 5.233e-01 1.911e+00 0.05246 5.219
stenose70-90% 6.354e-01 1.574e+00 0.08037 5.024
stenose90-99% 6.060e-01 1.650e+00 0.07448 4.930
stenose100% (Occlusion) 1.422e+00 7.034e-01 0.08147 24.808
stenoseNA NA NA NA NA
stenose50-99% 2.547e-07 3.926e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.672 (se = 0.034 )
Likelihood ratio test= 23.18 on 19 df, p=0.2
Wald test = 21.31 on 19 df, p=0.3
Score (logrank) test = 22.41 on 19 df, p=0.3
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.159428
Standard error............: 0.282444
Odds ratio (effect size)..: 1.173
Lower 95% CI..............: 0.674
Upper 95% CI..............: 2.04
T-value...................: 0.564459
P-value...................: 0.5724415
Sample size in model......: 1029
Number of events..........: 59
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 29
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -5.111e-01 5.998e-01 3.922e-01 -1.303 0.192
Age 1.682e-02 1.017e+00 2.487e-02 0.676 0.499
Gendermale 7.422e-02 1.077e+00 4.374e-01 0.170 0.865
ORdate_year -2.613e-02 9.742e-01 1.808e-01 -0.145 0.885
Hypertension.compositeno -8.185e-01 4.411e-01 7.527e-01 -1.087 0.277
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.130e-01 1.237e+00 4.580e-01 0.465 0.642
SmokerStatusEx-smoker -5.374e-01 5.843e-01 4.196e-01 -1.281 0.200
SmokerStatusNever smoked -3.304e-01 7.186e-01 6.147e-01 -0.537 0.591
Med.Statin.LLDno -6.489e-02 9.372e-01 4.561e-01 -0.142 0.887
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -8.493e-02 9.186e-01 6.709e-01 -0.127 0.899
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 2.245e-03 1.002e+00 1.037e-02 0.216 0.829
BMI -3.808e-03 9.962e-01 4.917e-02 -0.077 0.938
MedHx_CVDyes 2.618e-01 1.299e+00 4.162e-01 0.629 0.529
stenose0-49% -1.873e+01 7.320e-09 1.321e+04 -0.001 0.999
stenose50-70% -1.857e+01 8.632e-09 5.072e+03 -0.004 0.997
stenose70-90% -1.307e+00 2.705e-01 1.121e+00 -1.166 0.244
stenose90-99% -1.546e+00 2.130e-01 1.135e+00 -1.362 0.173
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 5.998e-01 1.667e+00 0.27811 1.294
Age 1.017e+00 9.833e-01 0.96858 1.068
Gendermale 1.077e+00 9.285e-01 0.45700 2.538
ORdate_year 9.742e-01 1.026e+00 0.68351 1.389
Hypertension.compositeno 4.411e-01 2.267e+00 0.10088 1.929
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.237e+00 8.081e-01 0.50429 3.036
SmokerStatusEx-smoker 5.843e-01 1.712e+00 0.25672 1.330
SmokerStatusNever smoked 7.186e-01 1.392e+00 0.21540 2.398
Med.Statin.LLDno 9.372e-01 1.067e+00 0.38338 2.291
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.186e-01 1.089e+00 0.24662 3.421
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.002e+00 9.978e-01 0.98209 1.023
BMI 9.962e-01 1.004e+00 0.90468 1.097
MedHx_CVDyes 1.299e+00 7.697e-01 0.57466 2.938
stenose0-49% 7.320e-09 1.366e+08 0.00000 Inf
stenose50-70% 8.632e-09 1.159e+08 0.00000 Inf
stenose70-90% 2.705e-01 3.697e+00 0.03004 2.436
stenose90-99% 2.130e-01 4.694e+00 0.02303 1.970
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.651 (se = 0.051 )
Likelihood ratio test= 9.82 on 17 df, p=0.9
Wald test = 7.29 on 17 df, p=1
Score (logrank) test = 9.15 on 17 df, p=0.9
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.511114
Standard error............: 0.392158
Odds ratio (effect size)..: 0.6
Lower 95% CI..............: 0.278
Upper 95% CI..............: 1.294
T-value...................: -1.303338
P-value...................: 0.1924592
Sample size in model......: 493
Number of events..........: 29
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 78
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 2.905e-01 1.337e+00 2.410e-01 1.205 0.228034
Age 3.225e-04 1.000e+00 1.532e-02 0.021 0.983210
Gendermale 8.268e-01 2.286e+00 3.041e-01 2.719 0.006547 **
ORdate_year -4.520e-02 9.558e-01 4.217e-02 -1.072 0.283803
Hypertension.compositeno -9.704e-01 3.789e-01 5.215e-01 -1.861 0.062793 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -8.006e-02 9.231e-01 2.760e-01 -0.290 0.771732
SmokerStatusEx-smoker -6.194e-01 5.383e-01 2.585e-01 -2.396 0.016592 *
SmokerStatusNever smoked -2.735e-01 7.607e-01 3.654e-01 -0.748 0.454249
Med.Statin.LLDno 5.528e-02 1.057e+00 2.760e-01 0.200 0.841276
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.320e-01 1.394e+00 3.353e-01 0.990 0.322098
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.003e-02 9.802e-01 5.954e-03 -3.365 0.000766 ***
BMI 1.495e-02 1.015e+00 3.351e-02 0.446 0.655552
MedHx_CVDyes 6.941e-01 2.002e+00 2.795e-01 2.483 0.013015 *
stenose0-49% -1.602e+01 1.106e-07 3.018e+03 -0.005 0.995765
stenose50-70% -1.801e+00 1.651e-01 1.427e+00 -1.262 0.206807
stenose70-90% -2.542e-01 7.756e-01 1.043e+00 -0.244 0.807516
stenose90-99% -3.387e-01 7.127e-01 1.054e+00 -0.321 0.747924
stenose100% (Occlusion) -1.545e+01 1.953e-07 2.480e+03 -0.006 0.995030
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 7.799e-01 2.181e+00 1.430e+00 0.545 0.585612
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.337e+00 7.479e-01 0.83375 2.1442
Age 1.000e+00 9.997e-01 0.97072 1.0308
Gendermale 2.286e+00 4.374e-01 1.25965 4.1490
ORdate_year 9.558e-01 1.046e+00 0.87999 1.0382
Hypertension.compositeno 3.789e-01 2.639e+00 0.13634 1.0532
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.231e-01 1.083e+00 0.53742 1.5854
SmokerStatusEx-smoker 5.383e-01 1.858e+00 0.32429 0.8935
SmokerStatusNever smoked 7.607e-01 1.315e+00 0.37170 1.5570
Med.Statin.LLDno 1.057e+00 9.462e-01 0.61526 1.8153
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.394e+00 7.175e-01 0.72237 2.6894
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.802e-01 1.020e+00 0.96879 0.9917
BMI 1.015e+00 9.852e-01 0.95054 1.0840
MedHx_CVDyes 2.002e+00 4.995e-01 1.15752 3.4620
stenose0-49% 1.106e-07 9.039e+06 0.00000 Inf
stenose50-70% 1.651e-01 6.056e+00 0.01008 2.7052
stenose70-90% 7.756e-01 1.289e+00 0.10037 5.9929
stenose90-99% 7.127e-01 1.403e+00 0.09030 5.6244
stenose100% (Occlusion) 1.953e-07 5.121e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.181e+00 4.584e-01 0.13215 36.0042
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.733 (se = 0.028 )
Likelihood ratio test= 51.75 on 19 df, p=7e-05
Wald test = 45.64 on 19 df, p=6e-04
Score (logrank) test = 49.2 on 19 df, p=2e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.290473
Standard error............: 0.240969
Odds ratio (effect size)..: 1.337
Lower 95% CI..............: 0.834
Upper 95% CI..............: 2.144
T-value...................: 1.205438
P-value...................: 0.2280341
Sample size in model......: 1029
Number of events..........: 78
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 42
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 3.996e-02 1.041e+00 3.269e-01 0.122 0.9027
Age 4.142e-02 1.042e+00 2.293e-02 1.806 0.0709 .
Gendermale 9.841e-01 2.675e+00 4.684e-01 2.101 0.0356 *
ORdate_year -2.760e-01 7.588e-01 1.478e-01 -1.867 0.0620 .
Hypertension.compositeno -2.340e-01 7.914e-01 5.431e-01 -0.431 0.6666
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 5.455e-01 1.726e+00 3.576e-01 1.526 0.1271
SmokerStatusEx-smoker -4.047e-01 6.672e-01 3.492e-01 -1.159 0.2465
SmokerStatusNever smoked -1.727e-02 9.829e-01 5.071e-01 -0.034 0.9728
Med.Statin.LLDno -5.702e-02 9.446e-01 3.626e-01 -0.157 0.8751
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 6.030e-01 1.828e+00 4.594e-01 1.313 0.1893
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.491e-02 9.852e-01 8.623e-03 -1.730 0.0837 .
BMI 4.034e-02 1.041e+00 4.216e-02 0.957 0.3387
MedHx_CVDyes 1.532e-01 1.166e+00 3.489e-01 0.439 0.6605
stenose0-49% -1.499e-01 8.608e-01 8.796e+03 0.000 1.0000
stenose50-70% 1.616e+01 1.041e+07 5.185e+03 0.003 0.9975
stenose70-90% 1.636e+01 1.269e+07 5.185e+03 0.003 0.9975
stenose90-99% 1.621e+01 1.100e+07 5.185e+03 0.003 0.9975
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 1.041e+00 9.608e-01 0.5484 1.975
Age 1.042e+00 9.594e-01 0.9965 1.090
Gendermale 2.675e+00 3.738e-01 1.0683 6.700
ORdate_year 7.588e-01 1.318e+00 0.5679 1.014
Hypertension.compositeno 7.914e-01 1.264e+00 0.2729 2.295
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.726e+00 5.795e-01 0.8562 3.478
SmokerStatusEx-smoker 6.672e-01 1.499e+00 0.3365 1.323
SmokerStatusNever smoked 9.829e-01 1.017e+00 0.3638 2.655
Med.Statin.LLDno 9.446e-01 1.059e+00 0.4641 1.923
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.828e+00 5.472e-01 0.7427 4.497
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.852e-01 1.015e+00 0.9687 1.002
BMI 1.041e+00 9.605e-01 0.9586 1.131
MedHx_CVDyes 1.166e+00 8.580e-01 0.5883 2.309
stenose0-49% 8.608e-01 1.162e+00 0.0000 Inf
stenose50-70% 1.041e+07 9.610e-08 0.0000 Inf
stenose70-90% 1.269e+07 7.881e-08 0.0000 Inf
stenose90-99% 1.100e+07 9.094e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.725 (se = 0.036 )
Likelihood ratio test= 25.04 on 17 df, p=0.09
Wald test = 15.97 on 17 df, p=0.5
Score (logrank) test = 24.24 on 17 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.039956
Standard error............: 0.326862
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.548
Upper 95% CI..............: 1.975
T-value...................: 0.122242
P-value...................: 0.9027075
Sample size in model......: 493
Number of events..........: 42
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 33
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 3.757e-02 1.038e+00 3.794e-01 0.099 0.921123
Age 7.047e-02 1.073e+00 2.723e-02 2.588 0.009658 **
Gendermale 1.226e+00 3.407e+00 5.594e-01 2.191 0.028427 *
ORdate_year -7.706e-02 9.258e-01 7.153e-02 -1.077 0.281331
Hypertension.compositeno -1.773e+01 2.000e-08 3.957e+03 -0.004 0.996425
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -9.565e-03 9.905e-01 4.279e-01 -0.022 0.982165
SmokerStatusEx-smoker -5.440e-01 5.804e-01 4.052e-01 -1.342 0.179449
SmokerStatusNever smoked -3.778e-01 6.854e-01 6.197e-01 -0.610 0.542134
Med.Statin.LLDno 1.675e-02 1.017e+00 4.225e-01 0.040 0.968375
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.115e+00 3.050e+00 4.178e-01 2.669 0.007602 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.284e-02 9.677e-01 9.422e-03 -3.485 0.000491 ***
BMI 8.583e-02 1.090e+00 5.240e-02 1.638 0.101466
MedHx_CVDyes 7.410e-01 2.098e+00 4.621e-01 1.603 0.108837
stenose0-49% -2.059e+01 1.144e-09 2.687e+04 -0.001 0.999389
stenose50-70% -1.271e+00 2.805e-01 1.263e+00 -1.007 0.314004
stenose70-90% -1.782e+00 1.683e-01 1.122e+00 -1.587 0.112409
stenose90-99% -1.497e+00 2.239e-01 1.150e+00 -1.301 0.193259
stenose100% (Occlusion) -1.989e+01 2.301e-09 1.983e+04 -0.001 0.999200
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.943e+01 3.629e-09 3.412e+04 -0.001 0.999546
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.038e+00 9.631e-01 0.49356 2.1842
Age 1.073e+00 9.320e-01 1.01724 1.1318
Gendermale 3.407e+00 2.935e-01 1.13818 10.1980
ORdate_year 9.258e-01 1.080e+00 0.80473 1.0652
Hypertension.compositeno 2.000e-08 5.000e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.905e-01 1.010e+00 0.42820 2.2911
SmokerStatusEx-smoker 5.804e-01 1.723e+00 0.26230 1.2843
SmokerStatusNever smoked 6.854e-01 1.459e+00 0.20343 2.3092
Med.Statin.LLDno 1.017e+00 9.834e-01 0.44427 2.3276
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.050e+00 3.278e-01 1.34493 6.9181
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.677e-01 1.033e+00 0.94999 0.9857
BMI 1.090e+00 9.178e-01 0.98326 1.2075
MedHx_CVDyes 2.098e+00 4.766e-01 0.84810 5.1899
stenose0-49% 1.144e-09 8.743e+08 0.00000 Inf
stenose50-70% 2.805e-01 3.566e+00 0.02361 3.3316
stenose70-90% 1.683e-01 5.941e+00 0.01865 1.5191
stenose90-99% 2.239e-01 4.466e+00 0.02349 2.1340
stenose100% (Occlusion) 2.301e-09 4.346e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.629e-09 2.755e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.844 (se = 0.031 )
Likelihood ratio test= 61.1 on 19 df, p=3e-06
Wald test = 21.88 on 19 df, p=0.3
Score (logrank) test = 57.18 on 19 df, p=1e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.037572
Standard error............: 0.37944
Odds ratio (effect size)..: 1.038
Lower 95% CI..............: 0.494
Upper 95% CI..............: 2.184
T-value...................: 0.099019
P-value...................: 0.921123
Sample size in model......: 1029
Number of events..........: 33
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 23
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -2.418e-01 7.852e-01 4.414e-01 -0.548 0.5839
Age 5.035e-02 1.052e+00 3.206e-02 1.571 0.1163
Gendermale 1.075e+00 2.930e+00 6.727e-01 1.598 0.1100
ORdate_year -1.134e-01 8.928e-01 1.951e-01 -0.581 0.5610
Hypertension.compositeno -1.802e+01 1.487e-08 4.552e+03 -0.004 0.9968
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 5.064e-01 1.659e+00 5.312e-01 0.953 0.3405
SmokerStatusEx-smoker -6.006e-01 5.485e-01 4.733e-01 -1.269 0.2045
SmokerStatusNever smoked -1.010e-01 9.039e-01 7.296e-01 -0.138 0.8899
Med.Statin.LLDno 7.451e-01 2.107e+00 4.587e-01 1.625 0.1043
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 5.400e-01 1.716e+00 6.750e-01 0.800 0.4237
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.044e-02 9.798e-01 1.039e-02 -1.968 0.0491 *
BMI 2.176e-02 1.022e+00 5.934e-02 0.367 0.7139
MedHx_CVDyes 1.312e+00 3.713e+00 6.416e-01 2.044 0.0409 *
stenose0-49% -7.481e-01 4.733e-01 3.720e+04 0.000 1.0000
stenose50-70% 4.915e-01 1.635e+00 2.257e+04 0.000 1.0000
stenose70-90% 1.839e+01 9.706e+07 2.097e+04 0.001 0.9993
stenose90-99% 1.809e+01 7.220e+07 2.097e+04 0.001 0.9993
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 7.852e-01 1.274e+00 0.3306 1.8651
Age 1.052e+00 9.509e-01 0.9876 1.1199
Gendermale 2.930e+00 3.413e-01 0.7839 10.9510
ORdate_year 8.928e-01 1.120e+00 0.6091 1.3086
Hypertension.compositeno 1.487e-08 6.727e+07 0.0000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.659e+00 6.027e-01 0.5858 4.7001
SmokerStatusEx-smoker 5.485e-01 1.823e+00 0.2169 1.3869
SmokerStatusNever smoked 9.039e-01 1.106e+00 0.2163 3.7768
Med.Statin.LLDno 2.107e+00 4.747e-01 0.8574 5.1763
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.716e+00 5.827e-01 0.4571 6.4433
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.798e-01 1.021e+00 0.9600 0.9999
BMI 1.022e+00 9.785e-01 0.9098 1.1480
MedHx_CVDyes 3.713e+00 2.693e-01 1.0557 13.0574
stenose0-49% 4.733e-01 2.113e+00 0.0000 Inf
stenose50-70% 1.635e+00 6.117e-01 0.0000 Inf
stenose70-90% 9.706e+07 1.030e-08 0.0000 Inf
stenose90-99% 7.220e+07 1.385e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.815 (se = 0.039 )
Likelihood ratio test= 33.08 on 17 df, p=0.01
Wald test = 12.37 on 17 df, p=0.8
Score (logrank) test = 27.71 on 17 df, p=0.05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.241771
Standard error............: 0.441385
Odds ratio (effect size)..: 0.785
Lower 95% CI..............: 0.331
Upper 95% CI..............: 1.865
T-value...................: -0.547755
P-value...................: 0.5838601
Sample size in model......: 493
Number of events..........: 23 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)object 'head.style' not found30-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
90-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
Correlations
We correlated plaque levels of the biomarkers.
MCP1 plaque levels
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA,
# select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
# TRAITS.BIN, TRAITS.CON.RANK)
# )
# AEDB.CEA.temp <- subset(AEDB.CEA,
# select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
# TRAITS.BIN, TRAITS.CON.RANK)
# )
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_pg_ml_2015_rank",
TRAITS.BIN,
TRAITS.CON.RANK,
"Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
str(AEDB.CEA.temp)'data.frame': 2423 obs. of 15 variables:
$ MCP1_pg_ml_2015_rank: num 0.45 2.497 0.09 1.572 0.652 ...
$ CalcificationPlaque : num 1 1 1 1 2 2 2 2 1 2 ...
$ CollagenPlaque : num 2 2 2 2 1 2 2 2 2 1 ...
$ Fat10Perc : num 2 2 2 2 2 2 2 1 2 2 ...
$ IPH : num 2 2 2 1 2 2 2 2 2 2 ...
$ MAC_binned : num 1 1 1 1 1 1 2 1 1 1 ...
$ SMC_binned : num 1 1 2 2 1 1 1 2 2 1 ...
$ Macrophages_rank : num 1.121 1.366 0.722 0.396 -1.013 ...
$ SMC_rank : num 1.13161 0.00148 1.42686 1.26957 0.34377 ...
$ MAC_SMC_ratio_rank : num 0.236 1.001 -0.139 -0.344 -1.245 ...
$ VesselDensity_rank : num -0.978 -0.774 0.717 1.1 1.518 ...
$ Symptoms.5G : num 5 5 6 6 5 6 1 5 2 5 ...
$ AsymptSympt : num 3 3 3 3 3 3 1 3 2 3 ...
$ EP_major : num 0 0 0 0 1 0 0 0 1 0 ...
$ EP_composite : num 2 2 2 2 3 2 3 2 3 2 ...AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)
NA# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")Loading required package: PerformanceAnalytics
Loading required package: xts
Loading required package: zoo
Attaching package: ‘zoo’
The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
Attaching package: ‘xts’
The following objects are masked from ‘package:data.table’:
first, last
The following objects are masked from ‘package:dplyr’:
first, last
Attaching package: ‘PerformanceAnalytics’
The following object is masked from ‘package:graphics’:
legendchart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")Loading required package: GGally
Registered S3 method overwritten by 'GGally':
method from
+.gg ggplot2
Attaching package: ‘GGally’
The following object is masked from ‘package:dplyr’:
nasa# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
ggpairs(AEDB.CEA,
columns = c("MCP1_pg_ml_2015_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
columnLabels = c("MCP1",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
"Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE)Extra arguments: 'method' are being ignored. If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.
Additional figures
Age and sex
We want to create per-age-group figures.
- Box and Whisker plot for MCP-1 plaque levels by sex.
- Box and Whisker plot for MCP-1 plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).
Inverse-rank transformed data
# ?ggpubr::ggboxplot()
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ Gender, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_pg_ml_2015_rank",
xlab = "gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Gender.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
# ?ggpubr::ggboxplot()
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ Gender, data = AEDB.CEA, method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_pg_ml_2015_rank",
xlab = "gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Gender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
Age >= 55 & Age <= 64 ~ "55-64",
Age >= 65 & Age <= 74 ~ "65-74",
Age >= 75 & Age <= 84 ~ "75-84",
Age >= 85 ~ "85+")))
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
Age >= 55 & Age <= 64 & Gender == "male"~ "55-64 males",
Age >= 65 & Age <= 74 & Gender == "male"~ "65-74 males",
Age >= 75 & Age <= 84 & Gender == "male"~ "75-84 males",
Age >= 85 & Gender == "male"~ "85+ males",
Age < 55 & Gender == "female" ~ "<55 females" ,
Age >= 55 & Age <= 64 & Gender == "female"~ "55-64 females ",
Age >= 65 & Age <= 74 & Gender == "female"~ "65-74 females",
Age >= 75 & Age <= 84 & Gender == "female"~ "75-84 females",
Age >= 85 & Gender == "female"~ "85+ females")))
table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
female male
<55 45 98
55-64 194 410
65-74 264 687
75-84 202 439
85+ 34 50table(AEDB.CEA$AgeGroupSex)
<55 females <55 males 55-64 females 55-64 males 65-74 females 65-74 males 75-84 females 75-84 males 85+ females 85+ males
45 98 194 410 264 687 202 439 34 50 Now we can draw some graphs of plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
# ?ggpubr::ggboxplot()
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Age groups (years)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "AgeGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ AgeGroup, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter",
ggthemne = theme_minimal()) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup_perGender.pdf"), plot = last_plot())Raw data
Simalarly but now for the raw data as median ± interquartile range.
# ?ggpubr::ggboxplot()
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Age groups (years)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "AgeGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ AgeGroup, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter",
ggthemne = theme_minimal()) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup_perGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Hypertension & blood pressure
We want to create figures of MCP1 levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs.
- Box and Whisker plot for MCP-1 plaque levels by hypertension group (no, yes)
- Box and Whisker plot for MCP-1 plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
systolic >= 120 & systolic <= 139 ~ "120-139",
systolic >= 140 & systolic <= 159 ~ "140-159",
systolic >= 160 ~ "160+")))
table(AEDB.CEA$SBPGroup, AEDB.CEA$Gender)
female male
<120 54 114
120-139 145 326
140-159 197 497
160+ 269 548Now we can draw some graphs of plaque MCP1 levels per sex and hypertension/blood pressure group.
Inverse-rank transformed data
compare_means(MCP1_pg_ml_2015_rank ~ SBPGroup, data = AEDB.CEA %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Systolic blood pressure (mmHg)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "SBPGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.selfreport, data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Self-reported hypertension",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypertension.selfreport",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.drugs, data = AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Hypertension medication use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.HypertensionDrugs.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ SBPGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup_byGender.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.selfreport, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Self-reported hypertension per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension_byGender.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.drugs, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Hypertension medication use per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ SBPGroup, group.by = "Hypertension.drugs", data = AEDB.CEA %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)),
x = c("SBPGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Systolic blood pressure (mmHg) by medication use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Self-reported hypertension by medication use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
compare_means(MCP1_pg_ml_2015_rank ~ SBPGroup, data = AEDB.CEA %>% filter(!is.na(SBPGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Systolic blood pressure (mmHg)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "SBPGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.selfreport, data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Self-reported hypertension",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypertension.selfreport",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.drugs, data = AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Hypertension medication use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.HypertensionDrugs.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ SBPGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(SBPGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.selfreport, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Self-reported hypertension per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.drugs, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Hypertension medication use per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ SBPGroup, group.by = "Hypertension.drugs", data = AEDB.CEA %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)),
x = c("SBPGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Systolic blood pressure (mmHg) by medication use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Self-reported hypertension by medication use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Hypercholesterolemia & LDL levels
We want to create figures of MCP1 levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs.
- Box and Whisker plot for MCP-1 plaque levels by hypercholesterolemia (
risk614) group (no, yes) - Box and Whisker plot for MCP-1 plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for MCP-1 plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
LDL_finalCU >= 100 & LDL_finalCU <= 129 ~ "100-129",
LDL_finalCU >= 130 & LDL_finalCU <= 159 ~ "130-159",
LDL_finalCU >= 160 & LDL_finalCU <= 189 ~ "160-189",
LDL_finalCU >= 190 ~ "190+")))
table(AEDB.CEA$LDLGroup, AEDB.CEA$Gender)
female male
<100 171 441
100-129 96 250
130-159 75 129
160-189 40 50
190+ 25 31require(sjlabelled)
AEDB.CEA$risk614 <- to_factor(AEDB.CEA$risk614)
# Fix plaquephenotypes
attach(AEDB.CEA)
AEDB.CEA[,"Hypercholesterolemia"] <- NA
AEDB.CEA$Hypercholesterolemia[risk614 == "missing value"] <- NA
AEDB.CEA$Hypercholesterolemia[risk614 == -999] <- NA
AEDB.CEA$Hypercholesterolemia[risk614 == "no"] <- "no"
AEDB.CEA$Hypercholesterolemia[risk614 == "yes"] <- "yes"
detach(AEDB.CEA)
table(AEDB.CEA$risk614, AEDB.CEA$Hypercholesterolemia)
no yes
missing value 0 0
no 648 0
yes 0 1563# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
Now we can draw some graphs of plaque MCP1 levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users.
Inverse-rank transformed data
compare_means(MCP1_pg_ml_2015_rank ~ LDLGroup, data = AEDB.CEA %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "LDLGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ LDLGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups_byGender.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Hypercholesterolemia, data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diagnosed hypercholesterolemia",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypercholesterolemia",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypercholesterolemia.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Hypercholesterolemia, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diagnosed hypercholesterolemia per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Med.Statin.LLD, data = AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Lipid-lowering drug use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Med.Statin.LLD.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Med.Statin.LLD, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Lipid-lowering drug use per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ LDLGroup, group.by = "Med.Statin.LLD", data = AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "LDL (mg/dL) per LLD use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)),
x = c("Hypercholesterolemia"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diagnosed hypercholesterolemia per LLD use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
compare_means(MCP1_pg_ml_2015_rank ~ LDLGroup, data = AEDB.CEA %>% filter(!is.na(LDLGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "LDLGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ LDLGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(LDLGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Hypercholesterolemia, data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diagnosed hypercholesterolemia",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Hypercholesterolemia",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypercholesterolemia.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Hypercholesterolemia, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diagnosed hypercholesterolemia per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Med.Statin.LLD, data = AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Lipid-lowering drug use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Med.Statin.LLD.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Med.Statin.LLD, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Lipid-lowering drug use per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ LDLGroup, group.by = "Med.Statin.LLD", data = AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "LDL (mg/dL) per LLD use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)),
x = c("Hypercholesterolemia"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diagnosed hypercholesterolemia per LLD use",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Kidney function (eGFR)
We want to create figures of MCP1 levels stratified by kidney function.
- Box and Whisker plot for MCP-1 plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for MCP-1 plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
GFR_MDRD >= 15 & GFR_MDRD <= 29 ~ "15-29",
GFR_MDRD >= 30 & GFR_MDRD <= 59 ~ "30-59",
GFR_MDRD >= 60 & GFR_MDRD <= 89 ~ "60-89",
GFR_MDRD >= 90 ~ "90+")))
table(AEDB.CEA$eGFRGroup, AEDB.CEA$Gender)
female male
<15 3 7
15-29 7 20
30-59 193 325
60-89 361 845
90+ 117 345table(AEDB.CEA$eGFRGroup, AEDB.CEA$KDOQI)
No data available/missing Normal kidney function CKD 2 (Mild) CKD 3 (Moderate) CKD 4 (Severe) CKD 5 (Failure)
<15 0 0 0 0 0 10
15-29 0 0 0 0 27 0
30-59 0 0 0 518 0 0
60-89 0 0 1206 0 0 0
90+ 0 462 0 0 0 0Now we can draw some graphs of plaque MCP1 levels per sex and kidney function group.
Inverse-rank transformed data
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ eGFRGroup, data = AEDB.CEA %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "eGFRGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ eGFRGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR_byGender.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ KDOQI, data = AEDB.CEA %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Kidney function (KDOQI)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.KDOQI.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ KDOQI, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Kidney function (KDOQI) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.KDOQI_byGender.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ eGFRGroup, data = AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR_KDOQI.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ eGFRGroup, data = AEDB.CEA %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "eGFRGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ eGFRGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ KDOQI, data = AEDB.CEA %>% filter(!is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Kidney function (KDOQI)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.KDOQI.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ KDOQI, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Kidney function (KDOQI) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.KDOQI_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ eGFRGroup, data = AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR_KDOQI.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
BMI
We want to create figures of MCP1 levels stratified by BMI.
- Box and Whisker plot for MCP-1 plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for MCP-1 plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
BMI >= 18.5 & BMI < 25 ~ "18.5-24",
BMI >= 25 & BMI < 30 ~ "25-29",
BMI >= 30 & BMI < 35 ~ "30-35",
BMI >= 35 ~ "35+")))
# require(labelled)
# AEDB.CEA$BMI_US <- as_factor(AEDB.CEA$BMI_US)
# AEDB.CEA$BMI_WHO <- as_factor(AEDB.CEA$BMI_WHO)
# table(AEDB.CEA$BMI_WHO, AEDB.CEA$BMI_US)
table(AEDB.CEA$BMIGroup, AEDB.CEA$Gender)
female male
<18.5 17 8
18.5-24 277 574
25-29 267 786
30-35 99 189
35+ 32 32table(AEDB.CEA$BMIGroup, AEDB.CEA$BMI_WHO)
No data available/missing Underweight Normal Overweight Obese
<18.5 0 24 0 0 0
18.5-24 0 0 851 0 0
25-29 0 0 0 1052 0
30-35 0 0 0 0 288
35+ 0 0 0 0 64Now we can draw some graphs of plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ BMIGroup, data = AEDB.CEA %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "BMIGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ BMIGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "BMI groups (kg/m2) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byGender.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ BMIGroup, data = AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("BMIGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byWHO.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ BMIGroup, data = AEDB.CEA %>% filter(!is.na(BMIGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "BMIGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ BMIGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(BMIGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "BMI groups (kg/m2) per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ BMIGroup, data = AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("BMIGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byWHO.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Diabetes
We want to create figures of MCP1 levels stratified by type 2 diabetes.
- Box and Whisker plot for MCP-1 plaque levels by type 2 diabetes group (no, yes)
Now we can draw some graphs of plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ DiabetesStatus, data = AEDB.CEA %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diabetes status",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "DiabetesStatus",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ DiabetesStatus, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diabetes status per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes_byGender.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ DiabetesStatus, data = AEDB.CEA %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diabetes status",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "DiabetesStatus",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ DiabetesStatus, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Diabetes status per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Smoking
We want to create figures of MCP1 levels stratified by smoking.
- Box and Whisker plot for MCP-1 plaque levels by smoking group (never, ex, current)
Now we can draw some graphs of plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ SmokerStatus, data = AEDB.CEA %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Smoker status",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "SmokerStatus",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ SmokerStatus, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Smoker status per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking_byGender.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ SmokerStatus, data = AEDB.CEA %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Smoker status",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "SmokerStatus",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ SmokerStatus, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Smoker status per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Stenosis
We want to create figures of MCP1 levels stratified by stenosis grade.
- Box and Whisker plot for MCP-1 plaque levels by stenosis grade group (<70, 70-89, 90+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
stenose == "0-49%" ~ "<70",
stenose == "50-70%" ~ "<70",
stenose == "70-90%" ~ "70-89",
stenose == "50-99%" ~ "90+",
stenose == "70-99%" ~ "90+",
stenose == "100% (Occlusion)" ~ "90+",
stenose == "90-99%" ~ "90+")))
table(AEDB.CEA$StenoticGroup, AEDB.CEA$Gender)
female male
<70 46 157
70-89 365 762
90+ 316 726table(AEDB.CEA$stenose, AEDB.CEA$StenoticGroup)
<70 70-89 90+
missing 0 0 0
0-49% 13 0 0
50-70% 190 0 0
70-90% 0 1127 0
90-99% 0 0 928
100% (Occlusion) 0 0 31
NA 0 0 0
50-99% 0 0 15
70-99% 0 0 68
99 0 0 0Now we can draw some graphs of plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ StenoticGroup, data = AEDB.CEA %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Stenotic grade",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "StenoticGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis.pdf"), plot = last_plot())
compare_means(MCP1_pg_ml_2015_rank ~ StenoticGroup, group.by ="Gender", data = AEDB.CEA %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Stenotic grade per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis_byGender.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ StenoticGroup, data = AEDB.CEA %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Stenotic grade",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "StenoticGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ StenoticGroup, group.by ="Gender", data = AEDB.CEA %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ml_2015_rank",
xlab = "Stenotic grade per gender",
ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels.
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1",
y = "MCP1_pg_ml_2015",
xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
ylab = "MCP1 plaque [pg/mL] (exp. no. 2)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plaque.raw.pdf"), plot = last_plot())
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ml_2015_rank",
xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
ylab = "MCP1 plaque [pg/mL] (exp. no. 2)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plaque.rank.pdf"), plot = last_plot())
Symptoms
We want to create per-symptom figures.
library(dplyr)
table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
<55 24 119
55-64 76 528
65-74 124 827
75-84 43 598
85+ 3 81table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
female 64 675
male 206 1478table(AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
270 2153 Inverse-rank transformed data
Now we can draw some graphs of plaque MCP1 levels per symptom group.
# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_pg_ml_2015_rank",
title = "MCP1 plaque [pg/mL] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 plaque [pg/mL]\n inverse-rank transformation",
color = "AsymptSympt2G",
# palette = c(uithof_color[16], uithof_color[23]),
palette = "npg",
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AsymptSympt2G.pdf"), plot = last_plot())
rm(p1)
compare_means(MCP1_pg_ml_2015_rank ~ AsymptSympt2G, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_pg_ml_2015_rank",
title = "MCP1 plaque [pg/mL] levels per symptom by gender",
xlab = "Symptoms",
ylab = "MCP1 plaque [pg/mL]\n inverse-rank transformation",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())
rm(p1)
Raw data
Simalarly but now for the raw data as median ± interquartile range.
# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_pg_ml_2015_rank",
title = "MCP1 plaque [pg/mL] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 plaque [pg/mL]\n inverse-rank transformation",
color = "AsymptSympt2G",
# palette = c(uithof_color[16], uithof_color[23]),
palette = "npg",
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AsymptSympt2G.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
compare_means(MCP1_pg_ml_2015_rank ~ AsymptSympt2G, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_pg_ml_2015_rank",
title = "MCP1 plaque [pg/mL] levels per symptom by gender",
xlab = "Symptoms",
ylab = "MCP1 plaque [pg/mL]\n inverse-rank transformation",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
Forest plots
We would also like to visualize the multivariable analyses results.
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"
models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1NAForest plot for experiment 2.
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ml_2015_rank"]),
low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ml_2015_rank"]),
high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ml_2015_rank"]))
fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) +
geom_hline(yintercept = 1, lty = 2) + # add a dotted line at x=1 after flip
coord_flip(ylim = c(0.8, 1.7)) + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
ggtitle("Plaque MCP-1 levels (1 SD increment, exp. #2, n = 1190+)") +
theme_minimal() # use a white background
print(fp)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.exp2.forest.pdf"), plot = fp)
rm(fp)Forest plot for experiment 1.
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))
fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) +
geom_hline(yintercept = 1, lty = 2) + # add a dotted line at x=1 after flip
coord_flip(ylim = c(0.8, 1.7)) + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
ggtitle("Plaque MCP-1 levels (1 SD increment, exp. #1, n = 490+)") +
theme_minimal() # use a white background
print(fp)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.exp1.forest.pdf"), plot = fp)
rm(fp)MCP1 vs. cytokines plaque levels correlations
We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:
- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB
In addition we will look at three metalloproteinases which were measured using an activity assay.
- MMP2
- MMP8
- MMP9
The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.
We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.
Prepare data
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21",
"INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1",
"TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"
proteins_of_interest <- c(cytokines, metalloproteinases)
proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))
# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
mutate_each(funs(as.numeric), proteins_of_interest)funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
[90mThis warning is displayed once per session.[39mNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(proteins_of_interest)` instead of `proteins_of_interest` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
for(PROTEIN in 1:length(proteins_of_interest)){
# UCORBIOGSAqc$Z <- NULL
var.temp.rank = proteins_of_interest_rank[PROTEIN]
var.temp = proteins_of_interest[PROTEIN]
cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
cat(paste0("* changing ", var.temp, " to numeric.\n"))
# AEDB.CEA <- AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))
AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA
cat(paste0("* standardising ", var.temp,
" (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
AEDB.CEA <- AEDB.CEA %>%
mutate_at(vars(var.temp),
# list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
)
# str(UCORBIOGSAqc$Z)
cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
AEDB.CEA[,var.temp.rank] <- NULL
names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}
Selecting IL2 and standardising: IL2_rank.
* changing IL2 to numeric.
* standardising IL2 (mean: 0.179942, n = 436).Note: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(var.temp)` instead of `var.temp` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m* renaming RANK to IL2_rank.
Selecting IL4 and standardising: IL4_rank.
* changing IL4 to numeric.
* standardising IL4 (mean: 0.167561, n = 406).
* renaming RANK to IL4_rank.
Selecting IL5 and standardising: IL5_rank.
* changing IL5 to numeric.
* standardising IL5 (mean: 0.178291, n = 432).
* renaming RANK to IL5_rank.
Selecting IL6 and standardising: IL6_rank.
* changing IL6 to numeric.
* standardising IL6 (mean: 0.188196, n = 456).
* renaming RANK to IL6_rank.
Selecting IL8 and standardising: IL8_rank.
* changing IL8 to numeric.
* standardising IL8 (mean: 0.182006, n = 441).
* renaming RANK to IL8_rank.
Selecting IL9 and standardising: IL9_rank.
* changing IL9 to numeric.
* standardising IL9 (mean: 0.206356, n = 500).
* renaming RANK to IL9_rank.
Selecting IL10 and standardising: IL10_rank.
* changing IL10 to numeric.
* standardising IL10 (mean: 0.158894, n = 385).
* renaming RANK to IL10_rank.
Selecting IL12 and standardising: IL12_rank.
* changing IL12 to numeric.
* standardising IL12 (mean: 0.167974, n = 407).
* renaming RANK to IL12_rank.
Selecting IL13 and standardising: IL13_rank.
* changing IL13 to numeric.
* standardising IL13 (mean: 0.232769, n = 564).
* renaming RANK to IL13_rank.
Selecting IL21 and standardising: IL21_rank.
* changing IL21 to numeric.
* standardising IL21 (mean: 0.233182, n = 565).
* renaming RANK to IL21_rank.
Selecting INFG and standardising: INFG_rank.
* changing INFG to numeric.
* standardising INFG (mean: 0.179117, n = 434).
* renaming RANK to INFG_rank.
Selecting TNFA and standardising: TNFA_rank.
* changing TNFA to numeric.
* standardising TNFA (mean: 0.163434, n = 396).
* renaming RANK to TNFA_rank.
Selecting MIF and standardising: MIF_rank.
* changing MIF to numeric.
* standardising MIF (mean: 0.233182, n = 565).
* renaming RANK to MIF_rank.
Selecting MCP1 and standardising: MCP1_rank.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.229468, n = 556).
* renaming RANK to MCP1_rank.
Selecting MIP1a and standardising: MIP1a_rank.
* changing MIP1a to numeric.
* standardising MIP1a (mean: 0.211721, n = 513).
* renaming RANK to MIP1a_rank.
Selecting RANTES and standardising: RANTES_rank.
* changing RANTES to numeric.
* standardising RANTES (mean: 0.228642, n = 554).
* renaming RANK to RANTES_rank.
Selecting MIG and standardising: MIG_rank.
* changing MIG to numeric.
* standardising MIG (mean: 0.226991, n = 550).
* renaming RANK to MIG_rank.
Selecting IP10 and standardising: IP10_rank.
* changing IP10 to numeric.
* standardising IP10 (mean: 0.205943, n = 499).
* renaming RANK to IP10_rank.
Selecting Eotaxin1 and standardising: Eotaxin1_rank.
* changing Eotaxin1 to numeric.
* standardising Eotaxin1 (mean: 0.233182, n = 565).
* renaming RANK to Eotaxin1_rank.
Selecting TARC and standardising: TARC_rank.
* changing TARC to numeric.
* standardising TARC (mean: 0.200578, n = 486).
* renaming RANK to TARC_rank.
Selecting PARC and standardising: PARC_rank.
* changing PARC to numeric.
* standardising PARC (mean: 0.233182, n = 565).
* renaming RANK to PARC_rank.
Selecting MDC and standardising: MDC_rank.
* changing MDC to numeric.
* standardising MDC (mean: 0.209657, n = 508).
* renaming RANK to MDC_rank.
Selecting OPG and standardising: OPG_rank.
* changing OPG to numeric.
* standardising OPG (mean: 0.232769, n = 564).
* renaming RANK to OPG_rank.
Selecting sICAM1 and standardising: sICAM1_rank.
* changing sICAM1 to numeric.
* standardising sICAM1 (mean: 0.233182, n = 565).
* renaming RANK to sICAM1_rank.
Selecting VEGFA and standardising: VEGFA_rank.
* changing VEGFA to numeric.
* standardising VEGFA (mean: 0.201403, n = 488).
* renaming RANK to VEGFA_rank.
Selecting TGFB and standardising: TGFB_rank.
* changing TGFB to numeric.
* standardising TGFB (mean: 0.22988, n = 557).
* renaming RANK to TGFB_rank.
Selecting MMP2 and standardising: MMP2_rank.
* changing MMP2 to numeric.
* standardising MMP2 (mean: 0.231944, n = 562).
* renaming RANK to MMP2_rank.
Selecting MMP8 and standardising: MMP8_rank.
* changing MMP8 to numeric.
* standardising MMP8 (mean: 0.231944, n = 562).
* renaming RANK to MMP8_rank.
Selecting MMP9 and standardising: MMP9_rank.
* changing MMP9 to numeric.
* standardising MMP9 (mean: 0.231531, n = 561).
* renaming RANK to MMP9_rank.# rm(var.temp, var.temp.rank)
Visualize transformations
We will just visualize these transformations.
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ml_2015_rank", proteins_of_interest_rank)
proteins_of_interest_mcp1 <- c("MCP1_pg_ml_2015", proteins_of_interest)
for(PROTEIN in proteins_of_interest_mcp1){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "",
ggtheme = theme_minimal())
print(p1)
}Plotting protein MCP1_pg_ml_2015.Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein IL2.
Plotting protein IL4.
Plotting protein IL5.
Plotting protein IL6.
Plotting protein IL8.
Plotting protein IL9.
Plotting protein IL10.
Plotting protein IL12.
Plotting protein IL13.
Plotting protein IL21.
Plotting protein INFG.
Plotting protein TNFA.
Plotting protein MIF.
Plotting protein MCP1.
Plotting protein MIP1a.
Plotting protein RANTES.
Plotting protein MIG.
Plotting protein IP10.
Plotting protein Eotaxin1.
Plotting protein TARC.
Plotting protein PARC.
Plotting protein MDC.
Plotting protein OPG.
Plotting protein sICAM1.
Plotting protein VEGFA.
Plotting protein TGFB.
Plotting protein MMP2.
Plotting protein MMP8.
Plotting protein MMP9.
for(PROTEIN in proteins_of_interest_rank_mcp1){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "inverse-normal transformation",
ggtheme = theme_minimal())
print(p1)
}Plotting protein MCP1_pg_ml_2015_rank.Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein IL2_rank.
Plotting protein IL4_rank.
Plotting protein IL5_rank.
Plotting protein IL6_rank.
Plotting protein IL8_rank.
Plotting protein IL9_rank.
Plotting protein IL10_rank.
Plotting protein IL12_rank.
Plotting protein IL13_rank.
Plotting protein IL21_rank.
Plotting protein INFG_rank.
Plotting protein TNFA_rank.
Plotting protein MIF_rank.
Plotting protein MCP1_rank.
Plotting protein MIP1a_rank.
Plotting protein RANTES_rank.
Plotting protein MIG_rank.
Plotting protein IP10_rank.
Plotting protein Eotaxin1_rank.
Plotting protein TARC_rank.
Plotting protein PARC_rank.
Plotting protein MDC_rank.
Plotting protein OPG_rank.
Plotting protein sICAM1_rank.
Plotting protein VEGFA_rank.
Plotting protein TGFB_rank.
Plotting protein MMP2_rank.
Plotting protein MMP8_rank.
Plotting protein MMP9_rank.
NACorrelations
Here we calculate correlations between MCP1_pg_ug_2015 and 28 other cytokines (including MCP1 as measured in experiment 1. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:
- L: LUMINEX
- E: ELISA
- a: activity assay
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c(proteins_of_interest_rank_mcp1)
)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2, pg/mL)",
"IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12",
"IL13 (L)", "IL21 (L)",
"INFG", "TNFA", "MIF (L)",
"MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)",
"Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)",
"OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"
DT::datatable(corr_biomarkers.rank.df)
fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
tl.cex = 6,
# xlab = c("MCP1"),
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in imageggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/mL)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold[1] 2.763428p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
ylab = expression("Spearman's"~italic(rho)),
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 0.8
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_exp2_vs_Cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_exp2_vs_Cytokines.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp1)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold[1] 2.763428p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
ylab = expression("Spearman's"~italic(rho)),
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 0.8
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_exp1_vs_Cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_exp1_vs_Cytokines.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
Another version - problably not good.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/mL)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold[1] 2.763428p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
color = "CytokineY", #fill = "CytokineY", # Color by groups
palette = uithof_color, # Custom color palette
xlab = "Cytokine",
ylab = expression(log[10]~"("~italic(p)~")-value"),
ylim = c(0, 9),
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = FALSE, # Rotate vertically
# group = "CytokineY", # Order by groups
dot.size = 8, # Large dot size
label = round(temp$SpearmanRho, digits = 3), # Add mpg values as dot labels
font.label = list(color = "white", size = 8,
vjust = 0.5)
)
ggpar(p1, legend = "",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.dotchart.MCP1_vs_Cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.dotchart.MCP1_vs_Cytokines.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(temp, p1)
MCP1 vs. cytokines plaque levels lm()
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines traits as a function of plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ml_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
439.52974 -0.08325 0.21160 -0.21965
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0911 -0.5531 -0.0990 0.4573 2.6749
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 450.388268 105.057037 4.287 2.37e-05 ***
currentDF[, TRAIT] -0.079670 0.049891 -1.597 0.1112
Age 0.006158 0.005594 1.101 0.2718
Gendermale 0.207882 0.105274 1.975 0.0491 *
ORdate_year -0.225272 0.052455 -4.295 2.29e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8921 on 336 degrees of freedom
Multiple R-squared: 0.06631, Adjusted R-squared: 0.0552
F-statistic: 5.966 on 4 and 336 DF, p-value: 0.0001197
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.07967
Standard error............: 0.049891
Odds ratio (effect size)..: 0.923
Lower 95% CI..............: 0.837
Upper 95% CI..............: 1.018
T-value...................: -1.596881
P-value...................: 0.1112322
R^2.......................: 0.066314
Adjusted r^2..............: 0.055199
Sample size of AE DB......: 2423
Sample size of model......: 341
Missing data %............: 85.92654
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
540.5738 -0.1171 0.1848 -0.2701
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0091 -0.5472 -0.1075 0.4856 2.6161
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 549.80994 111.55762 4.928 1.35e-06 ***
currentDF[, TRAIT] -0.11149 0.05084 -2.193 0.0291 *
Age 0.00713 0.00565 1.262 0.2079
Gendermale 0.17874 0.10843 1.648 0.1003
ORdate_year -0.27492 0.05570 -4.936 1.30e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8675 on 311 degrees of freedom
Multiple R-squared: 0.09242, Adjusted R-squared: 0.08074
F-statistic: 7.917 on 4 and 311 DF, p-value: 4.345e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.111487
Standard error............: 0.050839
Odds ratio (effect size)..: 0.895
Lower 95% CI..............: 0.81
Upper 95% CI..............: 0.988
T-value...................: -2.192931
P-value...................: 0.02905265
R^2.......................: 0.092417
Adjusted r^2..............: 0.080744
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
503.0037 0.2535 -0.2513
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.99155 -0.54478 -0.07638 0.50816 2.54186
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 537.457180 106.206389 5.060 6.93e-07 ***
currentDF[, TRAIT] -0.058497 0.048500 -1.206 0.2286
Age 0.007261 0.005456 1.331 0.1842
Gendermale 0.238482 0.103166 2.312 0.0214 *
ORdate_year -0.268776 0.053031 -5.068 6.67e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8697 on 333 degrees of freedom
Multiple R-squared: 0.08729, Adjusted R-squared: 0.07632
F-statistic: 7.962 on 4 and 333 DF, p-value: 3.862e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.058497
Standard error............: 0.0485
Odds ratio (effect size)..: 0.943
Lower 95% CI..............: 0.858
Upper 95% CI..............: 1.037
T-value...................: -1.206137
P-value...................: 0.2286207
R^2.......................: 0.087288
Adjusted r^2..............: 0.076324
Sample size of AE DB......: 2423
Sample size of model......: 338
Missing data %............: 86.05035
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
599.930101 0.073020 0.008033 0.319352 -0.300019
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8218 -0.5577 -0.1269 0.4803 2.9552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 599.930101 102.191239 5.871 1.02e-08 ***
currentDF[, TRAIT] 0.073020 0.048887 1.494 0.13617
Age 0.008033 0.005661 1.419 0.15682
Gendermale 0.319352 0.105481 3.028 0.00265 **
ORdate_year -0.300019 0.051030 -5.879 9.69e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9062 on 347 degrees of freedom
Multiple R-squared: 0.1111, Adjusted R-squared: 0.1008
F-statistic: 10.84 on 4 and 347 DF, p-value: 2.717e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.07302
Standard error............: 0.048887
Odds ratio (effect size)..: 1.076
Lower 95% CI..............: 0.977
Upper 95% CI..............: 1.184
T-value...................: 1.493662
P-value...................: 0.1361728
R^2.......................: 0.111087
Adjusted r^2..............: 0.10084
Sample size of AE DB......: 2423
Sample size of model......: 352
Missing data %............: 85.47255
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
697.4928 0.2632 0.3070 -0.3484
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1950 -0.5389 -0.0682 0.4695 2.8229
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 705.900501 95.458877 7.395 1.13e-12 ***
currentDF[, TRAIT] 0.260289 0.048081 5.414 1.18e-07 ***
Age 0.004083 0.005398 0.756 0.44998
Gendermale 0.309080 0.102061 3.028 0.00265 **
ORdate_year -0.352754 0.047664 -7.401 1.09e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8578 on 337 degrees of freedom
Multiple R-squared: 0.1946, Adjusted R-squared: 0.1851
F-statistic: 20.36 on 4 and 337 DF, p-value: 4.877e-15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.260289
Standard error............: 0.048081
Odds ratio (effect size)..: 1.297
Lower 95% CI..............: 1.181
Upper 95% CI..............: 1.426
T-value...................: 5.413579
P-value...................: 1.175805e-07
R^2.......................: 0.194641
Adjusted r^2..............: 0.185082
Sample size of AE DB......: 2423
Sample size of model......: 342
Missing data %............: 85.88527
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
392.8674 0.0749 0.3238 -0.1964
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0238 -0.5958 -0.1203 0.5313 2.9019
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 396.752588 88.227745 4.497 9.2e-06 ***
currentDF[, TRAIT] 0.077561 0.049808 1.557 0.1203
Age 0.002510 0.005644 0.445 0.6568
Gendermale 0.322682 0.104664 3.083 0.0022 **
ORdate_year -0.198406 0.044047 -4.504 8.9e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.944 on 376 degrees of freedom
Multiple R-squared: 0.07918, Adjusted R-squared: 0.06939
F-statistic: 8.083 on 4 and 376 DF, p-value: 2.921e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.077561
Standard error............: 0.049808
Odds ratio (effect size)..: 1.081
Lower 95% CI..............: 0.98
Upper 95% CI..............: 1.191
T-value...................: 1.557216
P-value...................: 0.1202604
R^2.......................: 0.079185
Adjusted r^2..............: 0.069389
Sample size of AE DB......: 2423
Sample size of model......: 381
Missing data %............: 84.27569
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
506.2501 0.2847 -0.2530
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.94534 -0.57186 -0.08303 0.49245 2.61786
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 532.461068 120.554405 4.417 1.41e-05 ***
currentDF[, TRAIT] -0.050056 0.054193 -0.924 0.3564
Age 0.006172 0.005829 1.059 0.2905
Gendermale 0.263904 0.112020 2.356 0.0191 *
ORdate_year -0.266274 0.060183 -4.424 1.36e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8827 on 296 degrees of freedom
Multiple R-squared: 0.08049, Adjusted R-squared: 0.06807
F-statistic: 6.478 on 4 and 296 DF, p-value: 5.213e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.050056
Standard error............: 0.054193
Odds ratio (effect size)..: 0.951
Lower 95% CI..............: 0.855
Upper 95% CI..............: 1.058
T-value...................: -0.923655
P-value...................: 0.356418
R^2.......................: 0.080495
Adjusted r^2..............: 0.068069
Sample size of AE DB......: 2423
Sample size of model......: 301
Missing data %............: 87.57738
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
548.41879 -0.08958 0.24946 -0.27403
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.96858 -0.54923 -0.09087 0.49552 2.65880
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 558.567899 112.807135 4.952 1.21e-06 ***
currentDF[, TRAIT] -0.083006 0.050937 -1.630 0.1042
Age 0.007858 0.005673 1.385 0.1670
Gendermale 0.236909 0.106987 2.214 0.0275 *
ORdate_year -0.279354 0.056321 -4.960 1.16e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8673 on 311 degrees of freedom
Multiple R-squared: 0.09053, Adjusted R-squared: 0.07883
F-statistic: 7.739 on 4 and 311 DF, p-value: 5.887e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.083006
Standard error............: 0.050937
Odds ratio (effect size)..: 0.92
Lower 95% CI..............: 0.833
Upper 95% CI..............: 1.017
T-value...................: -1.629574
P-value...................: 0.104204
R^2.......................: 0.090531
Adjusted r^2..............: 0.078833
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
408.84038 0.08096 0.26397 -0.20434
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.99901 -0.62198 -0.08861 0.53175 2.85876
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 417.755559 83.585739 4.998 8.46e-07 ***
currentDF[, TRAIT] 0.086418 0.045707 1.891 0.05934 .
Age 0.004672 0.005140 0.909 0.36389
Gendermale 0.260898 0.095552 2.730 0.00659 **
ORdate_year -0.208944 0.041733 -5.007 8.10e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9196 on 429 degrees of freedom
Multiple R-squared: 0.07553, Adjusted R-squared: 0.06691
F-statistic: 8.762 on 4 and 429 DF, p-value: 8.314e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.086418
Standard error............: 0.045707
Odds ratio (effect size)..: 1.09
Lower 95% CI..............: 0.997
Upper 95% CI..............: 1.192
T-value...................: 1.890677
P-value...................: 0.05934115
R^2.......................: 0.075528
Adjusted r^2..............: 0.066908
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
404.39405 0.06671 0.26022 -0.20212
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.98297 -0.61654 -0.06537 0.53462 2.88093
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 412.360885 83.370793 4.946 1.09e-06 ***
currentDF[, TRAIT] 0.071706 0.046064 1.557 0.12028
Age 0.004476 0.005115 0.875 0.38204
Gendermale 0.257574 0.095475 2.698 0.00725 **
ORdate_year -0.206243 0.041624 -4.955 1.04e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9198 on 430 degrees of freedom
Multiple R-squared: 0.07309, Adjusted R-squared: 0.06447
F-statistic: 8.477 on 4 and 430 DF, p-value: 1.368e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.071706
Standard error............: 0.046064
Odds ratio (effect size)..: 1.074
Lower 95% CI..............: 0.982
Upper 95% CI..............: 1.176
T-value...................: 1.556674
P-value...................: 0.1202837
R^2.......................: 0.073091
Adjusted r^2..............: 0.064469
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
624.40653 -0.09079 0.29372 -0.31196
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.95225 -0.57294 -0.08409 0.49377 2.82516
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 633.635976 109.178351 5.804 1.52e-08 ***
currentDF[, TRAIT] -0.086460 0.054878 -1.576 0.11609
Age 0.007030 0.005603 1.255 0.21048
Gendermale 0.291357 0.106617 2.733 0.00662 **
ORdate_year -0.316806 0.054506 -5.812 1.45e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8871 on 331 degrees of freedom
Multiple R-squared: 0.1125, Adjusted R-squared: 0.1017
F-statistic: 10.49 on 4 and 331 DF, p-value: 5.21e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.08646
Standard error............: 0.054878
Odds ratio (effect size)..: 0.917
Lower 95% CI..............: 0.824
Upper 95% CI..............: 1.021
T-value...................: -1.575507
P-value...................: 0.1160946
R^2.......................: 0.11247
Adjusted r^2..............: 0.101745
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
550.1031 0.2622 -0.2749
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.89787 -0.54287 -0.04567 0.49646 2.58480
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 576.966996 112.824162 5.114 5.63e-07 ***
currentDF[, TRAIT] -0.046024 0.050896 -0.904 0.3666
Age 0.007178 0.005690 1.262 0.2081
Gendermale 0.244660 0.106867 2.289 0.0227 *
ORdate_year -0.288518 0.056336 -5.121 5.42e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8595 on 301 degrees of freedom
Multiple R-squared: 0.09809, Adjusted R-squared: 0.08611
F-statistic: 8.184 on 4 and 301 DF, p-value: 2.814e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.046024
Standard error............: 0.050896
Odds ratio (effect size)..: 0.955
Lower 95% CI..............: 0.864
Upper 95% CI..............: 1.055
T-value...................: -0.904282
P-value...................: 0.3665688
R^2.......................: 0.098094
Adjusted r^2..............: 0.086109
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
402.1448 0.2706 -0.2010
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.91129 -0.60262 -0.06228 0.55583 2.96602
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 357.525975 91.070212 3.926 0.000101 ***
currentDF[, TRAIT] 0.067646 0.048881 1.384 0.167107
Age 0.004041 0.005094 0.793 0.428030
Gendermale 0.264634 0.095298 2.777 0.005727 **
ORdate_year -0.178868 0.045459 -3.935 9.71e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9203 on 430 degrees of freedom
Multiple R-squared: 0.072, Adjusted R-squared: 0.06337
F-statistic: 8.341 on 4 and 430 DF, p-value: 1.737e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.067646
Standard error............: 0.048881
Odds ratio (effect size)..: 1.07
Lower 95% CI..............: 0.972
Upper 95% CI..............: 1.178
T-value...................: 1.383899
P-value...................: 0.1671073
R^2.......................: 0.072001
Adjusted r^2..............: 0.063368
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
305.3839 0.2222 0.2393 -0.1527
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1835 -0.6174 -0.0939 0.5408 2.9516
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 313.648565 82.705400 3.792 0.000171 ***
currentDF[, TRAIT] 0.225302 0.043745 5.150 3.98e-07 ***
Age 0.004970 0.004986 0.997 0.319495
Gendermale 0.236381 0.093216 2.536 0.011574 *
ORdate_year -0.156994 0.041291 -3.802 0.000164 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8911 on 426 degrees of freedom
Multiple R-squared: 0.1224, Adjusted R-squared: 0.1142
F-statistic: 14.86 on 4 and 426 DF, p-value: 2.248e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.225302
Standard error............: 0.043745
Odds ratio (effect size)..: 1.253
Lower 95% CI..............: 1.15
Upper 95% CI..............: 1.365
T-value...................: 5.150371
P-value...................: 3.98173e-07
R^2.......................: 0.122428
Adjusted r^2..............: 0.114188
Sample size of AE DB......: 2423
Sample size of model......: 431
Missing data %............: 82.21213
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
393.90745 0.08625 0.30123 -0.19690
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0188 -0.6276 -0.0954 0.5264 2.8731
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 396.969693 86.616258 4.583 6.18e-06 ***
currentDF[, TRAIT] 0.087714 0.048336 1.815 0.07034 .
Age 0.001742 0.005496 0.317 0.75143
Gendermale 0.300124 0.102533 2.927 0.00362 **
ORdate_year -0.198485 0.043245 -4.590 6.00e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9358 on 389 degrees of freedom
Multiple R-squared: 0.07865, Adjusted R-squared: 0.06917
F-statistic: 8.301 on 4 and 389 DF, p-value: 1.964e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.087714
Standard error............: 0.048336
Odds ratio (effect size)..: 1.092
Lower 95% CI..............: 0.993
Upper 95% CI..............: 1.2
T-value...................: 1.814678
P-value...................: 0.0703435
R^2.......................: 0.078646
Adjusted r^2..............: 0.069172
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
327.62609 0.09623 0.26629 -0.16381
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.87164 -0.58387 -0.08473 0.52960 2.97018
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 332.983028 89.291714 3.729 0.000218 ***
currentDF[, TRAIT] 0.101528 0.047860 2.121 0.034475 *
Age 0.004957 0.005107 0.971 0.332343
Gendermale 0.262940 0.095951 2.740 0.006398 **
ORdate_year -0.166649 0.044571 -3.739 0.000210 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9144 on 422 degrees of freedom
Multiple R-squared: 0.07764, Adjusted R-squared: 0.0689
F-statistic: 8.881 on 4 and 422 DF, p-value: 6.824e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.101528
Standard error............: 0.04786
Odds ratio (effect size)..: 1.107
Lower 95% CI..............: 1.008
Upper 95% CI..............: 1.216
T-value...................: 2.121344
P-value...................: 0.03447519
R^2.......................: 0.077641
Adjusted r^2..............: 0.068898
Sample size of AE DB......: 2423
Sample size of model......: 427
Missing data %............: 82.37722
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
404.8686 0.2736 -0.2024
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9925 -0.5977 -0.1095 0.5154 2.9297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 427.718328 87.574695 4.884 1.48e-06 ***
currentDF[, TRAIT] 0.036122 0.047591 0.759 0.44828
Age 0.003277 0.005258 0.623 0.53344
Gendermale 0.267777 0.097077 2.758 0.00606 **
ORdate_year -0.213864 0.043728 -4.891 1.43e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.925 on 419 degrees of freedom
Multiple R-squared: 0.06996, Adjusted R-squared: 0.06108
F-statistic: 7.879 on 4 and 419 DF, p-value: 3.944e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.036122
Standard error............: 0.047591
Odds ratio (effect size)..: 1.037
Lower 95% CI..............: 0.944
Upper 95% CI..............: 1.138
T-value...................: 0.758996
P-value...................: 0.4482814
R^2.......................: 0.069959
Adjusted r^2..............: 0.06108
Sample size of AE DB......: 2423
Sample size of model......: 424
Missing data %............: 82.50103
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
400.3374 0.1025 0.2924 -0.2001
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.94733 -0.58274 -0.09808 0.53081 2.89664
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 407.032341 86.721660 4.694 3.74e-06 ***
currentDF[, TRAIT] 0.106331 0.047303 2.248 0.02515 *
Age 0.004060 0.005486 0.740 0.45972
Gendermale 0.289105 0.100577 2.874 0.00427 **
ORdate_year -0.203589 0.043296 -4.702 3.60e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9225 on 383 degrees of freedom
Multiple R-squared: 0.08553, Adjusted R-squared: 0.07598
F-statistic: 8.956 on 4 and 383 DF, p-value: 6.362e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.106331
Standard error............: 0.047303
Odds ratio (effect size)..: 1.112
Lower 95% CI..............: 1.014
Upper 95% CI..............: 1.22
T-value...................: 2.247891
P-value...................: 0.02515147
R^2.......................: 0.085535
Adjusted r^2..............: 0.075984
Sample size of AE DB......: 2423
Sample size of model......: 388
Missing data %............: 83.98679
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
402.1448 0.2706 -0.2010
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0014 -0.6096 -0.1007 0.5052 2.9228
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 417.170515 83.883143 4.973 9.53e-07 ***
currentDF[, TRAIT] 0.049261 0.046621 1.057 0.29128
Age 0.003995 0.005106 0.783 0.43434
Gendermale 0.257956 0.095913 2.689 0.00744 **
ORdate_year -0.208627 0.041881 -4.981 9.16e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9212 on 430 degrees of freedom
Multiple R-squared: 0.07028, Adjusted R-squared: 0.06163
F-statistic: 8.126 on 4 and 430 DF, p-value: 2.527e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.049261
Standard error............: 0.046621
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.959
Upper 95% CI..............: 1.151
T-value...................: 1.056626
P-value...................: 0.2912753
R^2.......................: 0.070282
Adjusted r^2..............: 0.061633
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
259.7313 0.1304 0.2372 -0.1299
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9737 -0.6141 -0.1072 0.5032 2.6997
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 266.159979 102.621677 2.594 0.00988 **
currentDF[, TRAIT] 0.133654 0.051245 2.608 0.00947 **
Age 0.003268 0.005404 0.605 0.54568
Gendermale 0.235606 0.102344 2.302 0.02189 *
ORdate_year -0.133251 0.051227 -2.601 0.00966 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9182 on 368 degrees of freedom
Multiple R-squared: 0.05708, Adjusted R-squared: 0.04683
F-statistic: 5.569 on 4 and 368 DF, p-value: 0.0002314
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.133654
Standard error............: 0.051245
Odds ratio (effect size)..: 1.143
Lower 95% CI..............: 1.034
Upper 95% CI..............: 1.264
T-value...................: 2.608132
P-value...................: 0.009474563
R^2.......................: 0.057077
Adjusted r^2..............: 0.046827
Sample size of AE DB......: 2423
Sample size of model......: 373
Missing data %............: 84.60586
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
327.4082 0.1384 0.2771 -0.1637
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9070 -0.6184 -0.0725 0.5392 3.0240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 333.667657 86.443553 3.860 0.000131 ***
currentDF[, TRAIT] 0.139435 0.046946 2.970 0.003144 **
Age 0.003913 0.005040 0.776 0.437991
Gendermale 0.275525 0.094515 2.915 0.003741 **
ORdate_year -0.166963 0.043154 -3.869 0.000126 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.913 on 430 degrees of freedom
Multiple R-squared: 0.08661, Adjusted R-squared: 0.07811
F-statistic: 10.19 on 4 and 430 DF, p-value: 6.827e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.139435
Standard error............: 0.046946
Odds ratio (effect size)..: 1.15
Lower 95% CI..............: 1.049
Upper 95% CI..............: 1.26
T-value...................: 2.970091
P-value...................: 0.003143855
R^2.......................: 0.086606
Adjusted r^2..............: 0.078109
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
408.0877 0.3213 -0.2040
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.99814 -0.59702 -0.08428 0.55183 2.92191
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 374.573357 92.251811 4.060 5.94e-05 ***
currentDF[, TRAIT] 0.057757 0.050953 1.134 0.25769
Age 0.001495 0.005455 0.274 0.78413
Gendermale 0.323557 0.102239 3.165 0.00168 **
ORdate_year -0.187304 0.046050 -4.067 5.77e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9354 on 386 degrees of freedom
Multiple R-squared: 0.07845, Adjusted R-squared: 0.0689
F-statistic: 8.215 on 4 and 386 DF, p-value: 2.29e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.057757
Standard error............: 0.050953
Odds ratio (effect size)..: 1.059
Lower 95% CI..............: 0.959
Upper 95% CI..............: 1.171
T-value...................: 1.133543
P-value...................: 0.2576896
R^2.......................: 0.078453
Adjusted r^2..............: 0.068903
Sample size of AE DB......: 2423
Sample size of model......: 391
Missing data %............: 83.86298
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
406.1916 0.1514 0.2463 -0.2030
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7926 -0.6021 -0.1021 0.5371 2.8144
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 414.852239 82.585723 5.023 7.47e-07 ***
currentDF[, TRAIT] 0.154488 0.043687 3.536 0.00045 ***
Age 0.004756 0.005058 0.940 0.34758
Gendermale 0.243240 0.094761 2.567 0.01060 *
ORdate_year -0.207492 0.041233 -5.032 7.14e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9102 on 429 degrees of freedom
Multiple R-squared: 0.09423, Adjusted R-squared: 0.08578
F-statistic: 11.16 on 4 and 429 DF, p-value: 1.28e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.154488
Standard error............: 0.043687
Odds ratio (effect size)..: 1.167
Lower 95% CI..............: 1.071
Upper 95% CI..............: 1.271
T-value...................: 3.536236
P-value...................: 0.0004500912
R^2.......................: 0.094227
Adjusted r^2..............: 0.085781
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
348.5754 0.1199 0.2743 -0.1743
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.94630 -0.60443 -0.07322 0.52075 2.92462
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 355.162519 84.910547 4.183 3.49e-05 ***
currentDF[, TRAIT] 0.126150 0.044725 2.821 0.00502 **
Age 0.005385 0.005088 1.058 0.29046
Gendermale 0.272232 0.094590 2.878 0.00420 **
ORdate_year -0.177736 0.042386 -4.193 3.34e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9139 on 430 degrees of freedom
Multiple R-squared: 0.0848, Adjusted R-squared: 0.07629
F-statistic: 9.961 on 4 and 430 DF, p-value: 1.023e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.12615
Standard error............: 0.044725
Odds ratio (effect size)..: 1.134
Lower 95% CI..............: 1.039
Upper 95% CI..............: 1.238
T-value...................: 2.820549
P-value...................: 0.00501602
R^2.......................: 0.0848
Adjusted r^2..............: 0.076287
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
625.1068 0.2088 0.2828 -0.3123
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8013 -0.5778 -0.1391 0.4550 2.8829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 637.536834 95.001315 6.711 7.46e-11 ***
currentDF[, TRAIT] 0.207733 0.047826 4.343 1.82e-05 ***
Age 0.004984 0.005203 0.958 0.33876
Gendermale 0.283464 0.099381 2.852 0.00459 **
ORdate_year -0.318649 0.047432 -6.718 7.14e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8676 on 363 degrees of freedom
Multiple R-squared: 0.1388, Adjusted R-squared: 0.1293
F-statistic: 14.62 on 4 and 363 DF, p-value: 4.399e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.207733
Standard error............: 0.047826
Odds ratio (effect size)..: 1.231
Lower 95% CI..............: 1.121
Upper 95% CI..............: 1.352
T-value...................: 4.343473
P-value...................: 1.822187e-05
R^2.......................: 0.138758
Adjusted r^2..............: 0.129267
Sample size of AE DB......: 2423
Sample size of model......: 368
Missing data %............: 84.81222
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
433.2702 0.2619 -0.2165
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9163 -0.6071 -0.1017 0.5428 2.9677
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 420.271319 86.614707 4.852 1.72e-06 ***
currentDF[, TRAIT] 0.048956 0.046165 1.060 0.28954
Age 0.003353 0.005156 0.650 0.51589
Gendermale 0.264868 0.097695 2.711 0.00698 **
ORdate_year -0.210145 0.043243 -4.860 1.66e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9269 on 420 degrees of freedom
Multiple R-squared: 0.07633, Adjusted R-squared: 0.06754
F-statistic: 8.677 on 4 and 420 DF, p-value: 9.758e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.048956
Standard error............: 0.046165
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.959
Upper 95% CI..............: 1.15
T-value...................: 1.060461
P-value...................: 0.2895444
R^2.......................: 0.076334
Adjusted r^2..............: 0.067537
Sample size of AE DB......: 2423
Sample size of model......: 425
Missing data %............: 82.45976
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
563.6482 0.3221 -0.2816
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9560 -0.5839 -0.0973 0.5118 3.1719
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 559.51950 88.02210 6.357 5.33e-10 ***
currentDF[, TRAIT] 0.03617 0.04616 0.784 0.433740
Age 0.00240 0.00506 0.474 0.635456
Gendermale 0.33046 0.09564 3.455 0.000605 ***
ORdate_year -0.27966 0.04394 -6.364 5.09e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9122 on 425 degrees of freedom
Multiple R-squared: 0.1113, Adjusted R-squared: 0.1029
F-statistic: 13.31 on 4 and 425 DF, p-value: 3.182e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.036166
Standard error............: 0.046157
Odds ratio (effect size)..: 1.037
Lower 95% CI..............: 0.947
Upper 95% CI..............: 1.135
T-value...................: 0.78355
P-value...................: 0.4337405
R^2.......................: 0.111296
Adjusted r^2..............: 0.102932
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
563.2526 0.1837 0.2579 -0.2814
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.14127 -0.54874 -0.08888 0.49583 3.08371
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 566.655197 85.910098 6.596 1.26e-10 ***
currentDF[, TRAIT] 0.183850 0.044968 4.089 5.19e-05 ***
Age 0.002163 0.004947 0.437 0.66212
Gendermale 0.257646 0.094569 2.724 0.00671 **
ORdate_year -0.283185 0.042890 -6.603 1.21e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8954 on 425 degrees of freedom
Multiple R-squared: 0.1437, Adjusted R-squared: 0.1356
F-statistic: 17.83 on 4 and 425 DF, p-value: 1.523e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.18385
Standard error............: 0.044968
Odds ratio (effect size)..: 1.202
Lower 95% CI..............: 1.1
Upper 95% CI..............: 1.313
T-value...................: 4.088516
P-value...................: 5.192815e-05
R^2.......................: 0.143692
Adjusted r^2..............: 0.135633
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
536.0842 0.1141 0.3002 -0.2679
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0536 -0.5754 -0.1183 0.5094 3.0413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 538.771644 87.598569 6.150 1.78e-09 ***
currentDF[, TRAIT] 0.113628 0.044259 2.567 0.01059 *
Age 0.001637 0.005008 0.327 0.74392
Gendermale 0.300070 0.094724 3.168 0.00165 **
ORdate_year -0.269268 0.043733 -6.157 1.72e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9058 on 425 degrees of freedom
Multiple R-squared: 0.1236, Adjusted R-squared: 0.1154
F-statistic: 14.99 on 4 and 425 DF, p-value: 1.817e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.113628
Standard error............: 0.044259
Odds ratio (effect size)..: 1.12
Lower 95% CI..............: 1.027
Upper 95% CI..............: 1.222
T-value...................: 2.567334
P-value...................: 0.01058951
R^2.......................: 0.123604
Adjusted r^2..............: 0.115355
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
Analysis of MCP1_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
317.7837 0.2414 -0.1587
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3756 -0.6561 -0.0206 0.6542 2.6675
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 331.200461 103.072538 3.213 0.00142 **
currentDF[, TRAIT] -0.067438 0.048698 -1.385 0.16687
Age -0.005142 0.005524 -0.931 0.35245
Gendermale 0.234123 0.107979 2.168 0.03073 *
ORdate_year -0.165194 0.051462 -3.210 0.00143 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9691 on 403 degrees of freedom
Multiple R-squared: 0.03965, Adjusted R-squared: 0.03011
F-statistic: 4.159 on 4 and 403 DF, p-value: 0.002591
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.067438
Standard error............: 0.048698
Odds ratio (effect size)..: 0.935
Lower 95% CI..............: 0.85
Upper 95% CI..............: 1.028
T-value...................: -1.384828
P-value...................: 0.1668713
R^2.......................: 0.039647
Adjusted r^2..............: 0.030115
Sample size of AE DB......: 2423
Sample size of model......: 408
Missing data %............: 83.16137
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
396.488 0.302 -0.198
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3451 -0.6586 -0.0216 0.6565 2.7193
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 395.419104 111.526359 3.546 0.000442 ***
currentDF[, TRAIT] -0.039830 0.050847 -0.783 0.433930
Age -0.006173 0.005785 -1.067 0.286646
Gendermale 0.299567 0.113355 2.643 0.008571 **
ORdate_year -0.197249 0.055683 -3.542 0.000447 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9702 on 373 degrees of freedom
Multiple R-squared: 0.05331, Adjusted R-squared: 0.04316
F-statistic: 5.251 on 4 and 373 DF, p-value: 0.0003999
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.03983
Standard error............: 0.050847
Odds ratio (effect size)..: 0.961
Lower 95% CI..............: 0.87
Upper 95% CI..............: 1.062
T-value...................: -0.783331
P-value...................: 0.43393
R^2.......................: 0.053309
Adjusted r^2..............: 0.043157
Sample size of AE DB......: 2423
Sample size of model......: 378
Missing data %............: 84.39951
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
384.48883 -0.07448 0.28809 -0.19198
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4804 -0.6335 -0.0382 0.6628 2.6508
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 369.073757 107.080598 3.447 0.000628 ***
currentDF[, TRAIT] -0.075767 0.048993 -1.546 0.122783
Age -0.007120 0.005603 -1.271 0.204576
Gendermale 0.299056 0.108116 2.766 0.005939 **
ORdate_year -0.184049 0.053468 -3.442 0.000638 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9656 on 396 degrees of freedom
Multiple R-squared: 0.05401, Adjusted R-squared: 0.04446
F-statistic: 5.653 on 4 and 396 DF, p-value: 0.0001964
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.075767
Standard error............: 0.048993
Odds ratio (effect size)..: 0.927
Lower 95% CI..............: 0.842
Upper 95% CI..............: 1.02
T-value...................: -1.546497
P-value...................: 0.1227834
R^2.......................: 0.054014
Adjusted r^2..............: 0.044458
Sample size of AE DB......: 2423
Sample size of model......: 401
Missing data %............: 83.45027
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
304.00325 0.06981 0.22677 -0.15178
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.15866 -0.65200 -0.00741 0.66318 2.69318
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 295.809907 97.929224 3.021 0.00268 **
currentDF[, TRAIT] 0.069665 0.047370 1.471 0.14213
Age -0.005063 0.005497 -0.921 0.35752
Gendermale 0.230037 0.106708 2.156 0.03167 *
ORdate_year -0.147522 0.048896 -3.017 0.00271 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9719 on 420 degrees of freedom
Multiple R-squared: 0.03519, Adjusted R-squared: 0.026
F-statistic: 3.83 on 4 and 420 DF, p-value: 0.004536
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.069665
Standard error............: 0.04737
Odds ratio (effect size)..: 1.072
Lower 95% CI..............: 0.977
Upper 95% CI..............: 1.176
T-value...................: 1.470669
P-value...................: 0.1421293
R^2.......................: 0.035189
Adjusted r^2..............: 0.026
Sample size of AE DB......: 2423
Sample size of model......: 425
Missing data %............: 82.45976
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
455.41647 0.31925 -0.01045 0.22219 -0.22697
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.15896 -0.54787 -0.05189 0.56747 2.83787
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 455.416470 91.719536 4.965 1.01e-06 ***
currentDF[, TRAIT] 0.319248 0.047186 6.766 4.63e-11 ***
Age -0.010455 0.005209 -2.007 0.0454 *
Gendermale 0.222191 0.102155 2.175 0.0302 *
ORdate_year -0.226974 0.045788 -4.957 1.05e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9134 on 407 degrees of freedom
Multiple R-squared: 0.1467, Adjusted R-squared: 0.1383
F-statistic: 17.49 on 4 and 407 DF, p-value: 2.941e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.319248
Standard error............: 0.047186
Odds ratio (effect size)..: 1.376
Lower 95% CI..............: 1.255
Upper 95% CI..............: 1.509
T-value...................: 6.765783
P-value...................: 4.631788e-11
R^2.......................: 0.146701
Adjusted r^2..............: 0.138315
Sample size of AE DB......: 2423
Sample size of model......: 412
Missing data %............: 82.99629
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
449.5529 0.2728 0.2753 -0.2244
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.01604 -0.62630 -0.08902 0.62907 2.66902
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 445.416806 71.222529 6.254 8.66e-10 ***
currentDF[, TRAIT] 0.269238 0.040045 6.723 4.91e-11 ***
Age -0.004330 0.004633 -0.935 0.35047
Gendermale 0.277452 0.088478 3.136 0.00182 **
ORdate_year -0.222210 0.035552 -6.250 8.85e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8896 on 495 degrees of freedom
Multiple R-squared: 0.1682, Adjusted R-squared: 0.1614
F-statistic: 25.02 on 4 and 495 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.269238
Standard error............: 0.040045
Odds ratio (effect size)..: 1.309
Lower 95% CI..............: 1.21
Upper 95% CI..............: 1.416
T-value...................: 6.723338
P-value...................: 4.907886e-11
R^2.......................: 0.168152
Adjusted r^2..............: 0.16143
Sample size of AE DB......: 2423
Sample size of model......: 500
Missing data %............: 79.36442
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
406.1557 -0.1134 0.2844 -0.2028
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4859 -0.6531 -0.0240 0.7019 2.6509
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 398.869702 119.403549 3.341 0.000926 ***
currentDF[, TRAIT] -0.117134 0.053607 -2.185 0.029543 *
Age -0.007922 0.005967 -1.328 0.185151
Gendermale 0.298223 0.116386 2.562 0.010810 *
ORdate_year -0.198902 0.059610 -3.337 0.000938 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9788 on 353 degrees of freedom
Multiple R-squared: 0.05845, Adjusted R-squared: 0.04778
F-statistic: 5.478 on 4 and 353 DF, p-value: 0.0002735
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.117134
Standard error............: 0.053607
Odds ratio (effect size)..: 0.889
Lower 95% CI..............: 0.801
Upper 95% CI..............: 0.988
T-value...................: -2.185044
P-value...................: 0.02954261
R^2.......................: 0.058451
Adjusted r^2..............: 0.047781
Sample size of AE DB......: 2423
Sample size of model......: 358
Missing data %............: 85.22493
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
393.10801 -0.09786 0.30519 -0.19629
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5214 -0.6373 -0.0335 0.6609 2.6820
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 385.201779 111.303850 3.461 0.000601 ***
currentDF[, TRAIT] -0.099846 0.050664 -1.971 0.049487 *
Age -0.005609 0.005764 -0.973 0.331137
Gendermale 0.316927 0.111700 2.837 0.004797 **
ORdate_year -0.192164 0.055571 -3.458 0.000607 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.966 on 374 degrees of freedom
Multiple R-squared: 0.05558, Adjusted R-squared: 0.04548
F-statistic: 5.503 on 4 and 374 DF, p-value: 0.0002583
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.099846
Standard error............: 0.050664
Odds ratio (effect size)..: 0.905
Lower 95% CI..............: 0.819
Upper 95% CI..............: 0.999
T-value...................: -1.970771
P-value...................: 0.04948712
R^2.......................: 0.055585
Adjusted r^2..............: 0.045484
Sample size of AE DB......: 2423
Sample size of model......: 379
Missing data %............: 84.35823
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
464.1716 0.4014 0.2097 -0.2317
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6625 -0.6651 -0.0686 0.5793 2.4990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 462.277252 69.049724 6.695 5.34e-11 ***
currentDF[, TRAIT] 0.400238 0.037890 10.563 < 2e-16 ***
Age -0.001378 0.004368 -0.315 0.7525
Gendermale 0.210670 0.083341 2.528 0.0118 *
ORdate_year -0.230718 0.034471 -6.693 5.40e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8808 on 550 degrees of freedom
Multiple R-squared: 0.2245, Adjusted R-squared: 0.2189
F-statistic: 39.81 on 4 and 550 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.400238
Standard error............: 0.03789
Odds ratio (effect size)..: 1.492
Lower 95% CI..............: 1.385
Upper 95% CI..............: 1.607
T-value...................: 10.56311
P-value...................: 7.144811e-24
R^2.......................: 0.224521
Adjusted r^2..............: 0.218881
Sample size of AE DB......: 2423
Sample size of model......: 555
Missing data %............: 77.09451
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
436.2035 0.3523 0.2078 -0.2178
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5943 -0.6710 -0.0755 0.6157 2.3777
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 432.376101 70.746565 6.112 1.87e-09 ***
currentDF[, TRAIT] 0.350079 0.038816 9.019 < 2e-16 ***
Age -0.002970 0.004468 -0.665 0.5065
Gendermale 0.209696 0.085522 2.452 0.0145 *
ORdate_year -0.215744 0.035317 -6.109 1.90e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9045 on 551 degrees of freedom
Multiple R-squared: 0.1873, Adjusted R-squared: 0.1814
F-statistic: 31.76 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.350079
Standard error............: 0.038816
Odds ratio (effect size)..: 1.419
Lower 95% CI..............: 1.315
Upper 95% CI..............: 1.531
T-value...................: 9.018962
P-value...................: 3.135903e-18
R^2.......................: 0.187347
Adjusted r^2..............: 0.181448
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
435.5907 0.3678 -0.2175
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4331 -0.6404 -0.0233 0.6720 2.7166
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 471.147317 106.339563 4.431 1.21e-05 ***
currentDF[, TRAIT] -0.062392 0.051283 -1.217 0.224459
Age -0.004974 0.005589 -0.890 0.374066
Gendermale 0.360477 0.108341 3.327 0.000958 ***
ORdate_year -0.235101 0.053086 -4.429 1.23e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9702 on 400 degrees of freedom
Multiple R-squared: 0.07276, Adjusted R-squared: 0.06349
F-statistic: 7.848 on 4 and 400 DF, p-value: 4.264e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.062392
Standard error............: 0.051283
Odds ratio (effect size)..: 0.94
Lower 95% CI..............: 0.85
Upper 95% CI..............: 1.039
T-value...................: -1.216638
P-value...................: 0.2244592
R^2.......................: 0.072765
Adjusted r^2..............: 0.063493
Sample size of AE DB......: 2423
Sample size of model......: 405
Missing data %............: 83.28518
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
321.0628 0.2653 -0.1603
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2762 -0.6483 0.0060 0.6411 2.8309
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 308.664496 112.145327 2.752 0.00621 **
currentDF[, TRAIT] 0.009713 0.050789 0.191 0.84844
Age -0.006370 0.005821 -1.094 0.27459
Gendermale 0.277185 0.112881 2.456 0.01453 *
ORdate_year -0.153941 0.055991 -2.749 0.00627 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9721 on 365 degrees of freedom
Multiple R-squared: 0.03932, Adjusted R-squared: 0.02879
F-statistic: 3.734 on 4 and 365 DF, p-value: 0.005412
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TNFA_rank
Effect size...............: 0.009713
Standard error............: 0.050789
Odds ratio (effect size)..: 1.01
Lower 95% CI..............: 0.914
Upper 95% CI..............: 1.115
T-value...................: 0.191241
P-value...................: 0.8484435
R^2.......................: 0.039317
Adjusted r^2..............: 0.028789
Sample size of AE DB......: 2423
Sample size of model......: 370
Missing data %............: 84.72967
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
136.44181 0.34462 0.23501 -0.06817
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3632 -0.6092 -0.0177 0.6642 2.6913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 133.066491 79.750174 1.669 0.09578 .
currentDF[, TRAIT] 0.342095 0.043437 7.876 1.81e-14 ***
Age -0.004360 0.004529 -0.963 0.33605
Gendermale 0.237429 0.086694 2.739 0.00637 **
ORdate_year -0.066341 0.039805 -1.667 0.09615 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9186 on 551 degrees of freedom
Multiple R-squared: 0.1617, Adjusted R-squared: 0.1557
F-statistic: 26.58 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.342095
Standard error............: 0.043437
Odds ratio (effect size)..: 1.408
Lower 95% CI..............: 1.293
Upper 95% CI..............: 1.533
T-value...................: 7.875623
P-value...................: 1.814862e-14
R^2.......................: 0.161741
Adjusted r^2..............: 0.155655
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MCP1_rankattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
1.307e-34 1.000e+00 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.135e-16 -2.660e-17 -4.490e-18 1.675e-17 3.141e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.958e-15 1.141e-14 6.970e-01 0.486
currentDF[, TRAIT] 1.000e+00 6.259e-18 1.598e+17 <2e-16 ***
Age 8.099e-19 7.018e-19 1.154e+00 0.249
Gendermale 5.908e-18 1.353e-17 4.370e-01 0.662
ORdate_year -4.000e-18 5.698e-18 -7.020e-01 0.483
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.424e-16 on 551 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 6.842e+33 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MCP1_rank ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 1
Standard error............: 0
Odds ratio (effect size)..: 2.718
Lower 95% CI..............: 2.718
Upper 95% CI..............: 2.718
T-value...................: 1.59758e+17
P-value...................: 0
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
474.0856 0.3358 0.2006 -0.2366
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.79686 -0.65092 -0.06877 0.58497 2.67954
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 468.332834 69.013614 6.786 3.22e-11 ***
currentDF[, TRAIT] 0.332995 0.038566 8.635 < 2e-16 ***
Age -0.005195 0.004459 -1.165 0.2446
Gendermale 0.204041 0.085614 2.383 0.0175 *
ORdate_year -0.233591 0.034451 -6.780 3.34e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8684 on 508 degrees of freedom
Multiple R-squared: 0.2051, Adjusted R-squared: 0.1988
F-statistic: 32.77 on 4 and 508 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.332995
Standard error............: 0.038566
Odds ratio (effect size)..: 1.395
Lower 95% CI..............: 1.294
Upper 95% CI..............: 1.505
T-value...................: 8.634514
P-value...................: 7.646036e-17
R^2.......................: 0.205089
Adjusted r^2..............: 0.19883
Sample size of AE DB......: 2423
Sample size of model......: 513
Missing data %............: 78.8279
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
202.9668 0.3373 0.2082 -0.1014
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2792 -0.5356 -0.0162 0.5823 3.0478
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 202.399940 76.702752 2.639 0.00856 **
currentDF[, TRAIT] 0.336096 0.042438 7.920 1.35e-14 ***
Age -0.001053 0.004542 -0.232 0.81680
Gendermale 0.208992 0.086714 2.410 0.01628 *
ORdate_year -0.101037 0.038283 -2.639 0.00855 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.912 on 544 degrees of freedom
Multiple R-squared: 0.1653, Adjusted R-squared: 0.1591
F-statistic: 26.93 on 4 and 544 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.336096
Standard error............: 0.042438
Odds ratio (effect size)..: 1.399
Lower 95% CI..............: 1.288
Upper 95% CI..............: 1.521
T-value...................: 7.919657
P-value...................: 1.348892e-14
R^2.......................: 0.165283
Adjusted r^2..............: 0.159146
Sample size of AE DB......: 2423
Sample size of model......: 549
Missing data %............: 77.34214
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
570.9520 0.2940 0.2282 -0.2850
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2468 -0.6400 -0.0431 0.6164 2.4929
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 564.938294 74.957612 7.537 2.04e-13 ***
currentDF[, TRAIT] 0.290517 0.041532 6.995 7.86e-12 ***
Age -0.003719 0.004609 -0.807 0.42009
Gendermale 0.230474 0.087474 2.635 0.00866 **
ORdate_year -0.281872 0.037422 -7.532 2.10e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9197 on 541 degrees of freedom
Multiple R-squared: 0.1494, Adjusted R-squared: 0.1432
F-statistic: 23.76 on 4 and 541 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.290517
Standard error............: 0.041532
Odds ratio (effect size)..: 1.337
Lower 95% CI..............: 1.233
Upper 95% CI..............: 1.451
T-value...................: 6.995086
P-value...................: 7.859583e-12
R^2.......................: 0.149445
Adjusted r^2..............: 0.143156
Sample size of AE DB......: 2423
Sample size of model......: 546
Missing data %............: 77.46595
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
410.1428 0.4186 0.2370 -0.2047
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2812 -0.6240 -0.0607 0.5845 2.3274
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 408.108584 69.680872 5.857 8.64e-09 ***
currentDF[, TRAIT] 0.417366 0.038927 10.722 < 2e-16 ***
Age -0.002132 0.004490 -0.475 0.63514
Gendermale 0.238554 0.084533 2.822 0.00497 **
ORdate_year -0.203660 0.034782 -5.855 8.71e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8559 on 492 degrees of freedom
Multiple R-squared: 0.2476, Adjusted R-squared: 0.2415
F-statistic: 40.48 on 4 and 492 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.417366
Standard error............: 0.038927
Odds ratio (effect size)..: 1.518
Lower 95% CI..............: 1.406
Upper 95% CI..............: 1.638
T-value...................: 10.72171
P-value...................: 3.021844e-24
R^2.......................: 0.247632
Adjusted r^2..............: 0.241515
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
480.4459 0.3284 0.1907 -0.2398
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4214 -0.6698 -0.0780 0.6000 2.5515
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 474.710009 71.895573 6.603 9.51e-11 ***
currentDF[, TRAIT] 0.326133 0.039426 8.272 9.95e-16 ***
Age -0.004255 0.004505 -0.944 0.345
Gendermale 0.193342 0.086639 2.232 0.026 *
ORdate_year -0.236820 0.035891 -6.598 9.78e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9139 on 551 degrees of freedom
Multiple R-squared: 0.1704, Adjusted R-squared: 0.1644
F-statistic: 28.29 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.326133
Standard error............: 0.039426
Odds ratio (effect size)..: 1.386
Lower 95% CI..............: 1.283
Upper 95% CI..............: 1.497
T-value...................: 8.272003
P-value...................: 9.945147e-16
R^2.......................: 0.170403
Adjusted r^2..............: 0.16438
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
244.2877 0.2704 0.1328 -0.1220
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.95046 -0.61035 -0.07473 0.62130 2.69438
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 240.766099 89.076405 2.703 0.00712 **
currentDF[, TRAIT] 0.267944 0.044061 6.081 2.45e-09 ***
Age -0.002735 0.004820 -0.567 0.57068
Gendermale 0.133451 0.093203 1.432 0.15285
ORdate_year -0.120119 0.044459 -2.702 0.00714 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.922 on 476 degrees of freedom
Multiple R-squared: 0.1124, Adjusted R-squared: 0.1049
F-statistic: 15.07 on 4 and 476 DF, p-value: 1.324e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.267944
Standard error............: 0.044061
Odds ratio (effect size)..: 1.307
Lower 95% CI..............: 1.199
Upper 95% CI..............: 1.425
T-value...................: 6.08117
P-value...................: 2.451314e-09
R^2.......................: 0.112375
Adjusted r^2..............: 0.104915
Sample size of AE DB......: 2423
Sample size of model......: 481
Missing data %............: 80.14858
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
162.585286 0.425915 -0.006632 0.277017 -0.081011
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.11016 -0.55126 -0.02245 0.58456 2.17519
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 162.585286 72.281906 2.249 0.024886 *
currentDF[, TRAIT] 0.425915 0.039168 10.874 < 2e-16 ***
Age -0.006632 0.004326 -1.533 0.125889
Gendermale 0.277017 0.082886 3.342 0.000888 ***
ORdate_year -0.081011 0.036082 -2.245 0.025152 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8792 on 551 degrees of freedom
Multiple R-squared: 0.2322, Adjusted R-squared: 0.2266
F-statistic: 41.65 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.425915
Standard error............: 0.039168
Odds ratio (effect size)..: 1.531
Lower 95% CI..............: 1.418
Upper 95% CI..............: 1.653
T-value...................: 10.87395
P-value...................: 4.394713e-25
R^2.......................: 0.232156
Adjusted r^2..............: 0.226582
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
253.6058 0.3792 0.2858 -0.1266
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9082 -0.6351 -0.0943 0.5437 3.2873
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 250.003830 72.176641 3.464 0.000578 ***
currentDF[, TRAIT] 0.377012 0.040358 9.342 < 2e-16 ***
Age -0.004058 0.004424 -0.917 0.359527
Gendermale 0.288455 0.084892 3.398 0.000733 ***
ORdate_year -0.124711 0.036026 -3.462 0.000583 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8631 on 503 degrees of freedom
Multiple R-squared: 0.2225, Adjusted R-squared: 0.2163
F-statistic: 35.99 on 4 and 503 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.377012
Standard error............: 0.040358
Odds ratio (effect size)..: 1.458
Lower 95% CI..............: 1.347
Upper 95% CI..............: 1.578
T-value...................: 9.341649
P-value...................: 3.06886e-19
R^2.......................: 0.222496
Adjusted r^2..............: 0.216313
Sample size of AE DB......: 2423
Sample size of model......: 508
Missing data %............: 79.03426
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
440.2481 0.5843 0.1648 -0.2198
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6155 -0.5348 -0.0631 0.4750 2.5142
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 438.288904 60.992665 7.186 2.18e-12 ***
currentDF[, TRAIT] 0.583484 0.033999 17.162 < 2e-16 ***
Age -0.001501 0.003857 -0.389 0.6972
Gendermale 0.165816 0.073853 2.245 0.0252 *
ORdate_year -0.218731 0.030448 -7.184 2.22e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7796 on 550 degrees of freedom
Multiple R-squared: 0.3925, Adjusted R-squared: 0.3881
F-statistic: 88.84 on 4 and 550 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.583484
Standard error............: 0.033999
Odds ratio (effect size)..: 1.792
Lower 95% CI..............: 1.677
Upper 95% CI..............: 1.916
T-value...................: 17.16184
P-value...................: 3.464325e-53
R^2.......................: 0.392512
Adjusted r^2..............: 0.388094
Sample size of AE DB......: 2423
Sample size of model......: 555
Missing data %............: 77.09451
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
100.25480 0.65142 0.23469 -0.05012
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.12956 -0.37673 0.03337 0.42560 2.24890
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 101.612492 59.566339 1.706 0.088596 .
currentDF[, TRAIT] 0.653254 0.032594 20.042 < 2e-16 ***
Age 0.001807 0.003650 0.495 0.620785
Gendermale 0.233698 0.069490 3.363 0.000824 ***
ORdate_year -0.050855 0.029732 -1.710 0.087739 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7369 on 551 degrees of freedom
Multiple R-squared: 0.4606, Adjusted R-squared: 0.4567
F-statistic: 117.6 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.653254
Standard error............: 0.032594
Odds ratio (effect size)..: 1.922
Lower 95% CI..............: 1.803
Upper 95% CI..............: 2.049
T-value...................: 20.042
P-value...................: 1.602312e-67
R^2.......................: 0.460602
Adjusted r^2..............: 0.456687
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
636.0065 0.3152 0.2101 -0.3175
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2726 -0.6299 -0.0513 0.6024 2.7533
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 627.435812 87.528538 7.168 3.16e-12 ***
currentDF[, TRAIT] 0.316635 0.046349 6.832 2.76e-11 ***
Age -0.005637 0.004958 -1.137 0.2562
Gendermale 0.209258 0.096821 2.161 0.0312 *
ORdate_year -0.312989 0.043693 -7.163 3.26e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9106 on 447 degrees of freedom
Multiple R-squared: 0.1538, Adjusted R-squared: 0.1463
F-statistic: 20.32 on 4 and 447 DF, p-value: 2.158e-15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.316635
Standard error............: 0.046349
Odds ratio (effect size)..: 1.373
Lower 95% CI..............: 1.253
Upper 95% CI..............: 1.503
T-value...................: 6.831545
P-value...................: 2.757679e-11
R^2.......................: 0.153843
Adjusted r^2..............: 0.146271
Sample size of AE DB......: 2423
Sample size of model......: 452
Missing data %............: 81.34544
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
370.4817 0.1160 0.2732 -0.1850
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4713 -0.6428 -0.0446 0.6781 2.6902
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 366.409129 78.009902 4.697 3.38e-06 ***
currentDF[, TRAIT] 0.114467 0.042999 2.662 0.00800 **
Age -0.003384 0.004812 -0.703 0.48216
Gendermale 0.274764 0.092739 2.963 0.00319 **
ORdate_year -0.182825 0.038942 -4.695 3.41e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9567 on 523 degrees of freedom
Multiple R-squared: 0.07951, Adjusted R-squared: 0.07247
F-statistic: 11.29 on 4 and 523 DF, p-value: 8.501e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.114467
Standard error............: 0.042999
Odds ratio (effect size)..: 1.121
Lower 95% CI..............: 1.031
Upper 95% CI..............: 1.22
T-value...................: 2.662105
P-value...................: 0.008004355
R^2.......................: 0.079508
Adjusted r^2..............: 0.072468
Sample size of AE DB......: 2423
Sample size of model......: 528
Missing data %............: 78.20883
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
289.5407 0.3330 0.3622 -0.1446
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4835 -0.5786 0.0060 0.5934 2.6531
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 284.458423 77.752159 3.659 0.000279 ***
currentDF[, TRAIT] 0.330774 0.040358 8.196 1.91e-15 ***
Age -0.005165 0.004588 -1.126 0.260745
Gendermale 0.364122 0.088326 4.122 4.35e-05 ***
ORdate_year -0.141897 0.038812 -3.656 0.000282 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9102 on 526 degrees of freedom
Multiple R-squared: 0.1664, Adjusted R-squared: 0.16
F-statistic: 26.24 on 4 and 526 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.330774
Standard error............: 0.040358
Odds ratio (effect size)..: 1.392
Lower 95% CI..............: 1.286
Upper 95% CI..............: 1.507
T-value...................: 8.196056
P-value...................: 1.905934e-15
R^2.......................: 0.166354
Adjusted r^2..............: 0.160015
Sample size of AE DB......: 2423
Sample size of model......: 531
Missing data %............: 78.08502
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
389.774698 0.432719 -0.007878 0.143507 -0.194289
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2706 -0.4946 0.0294 0.5473 2.8701
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 389.774698 73.252460 5.321 1.53e-07 ***
currentDF[, TRAIT] 0.432719 0.038141 11.345 < 2e-16 ***
Age -0.007878 0.004362 -1.806 0.0715 .
Gendermale 0.143507 0.084864 1.691 0.0914 .
ORdate_year -0.194289 0.036567 -5.313 1.59e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8664 on 526 degrees of freedom
Multiple R-squared: 0.2447, Adjusted R-squared: 0.239
F-statistic: 42.61 on 4 and 526 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.432719
Standard error............: 0.038141
Odds ratio (effect size)..: 1.541
Lower 95% CI..............: 1.43
Upper 95% CI..............: 1.661
T-value...................: 11.34524
P-value...................: 7.740327e-27
R^2.......................: 0.244712
Adjusted r^2..............: 0.238968
Sample size of AE DB......: 2423
Sample size of model......: 531
Missing data %............: 78.08502
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
257.60136 0.56444 -0.01056 0.20596 -0.12826
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1884 -0.5048 -0.0073 0.5197 2.8723
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 257.601363 67.289335 3.828 0.000145 ***
currentDF[, TRAIT] 0.564438 0.035147 16.059 < 2e-16 ***
Age -0.010561 0.003989 -2.648 0.008352 **
Gendermale 0.205960 0.076739 2.684 0.007507 **
ORdate_year -0.128262 0.033591 -3.818 0.000150 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7912 on 525 degrees of freedom
Multiple R-squared: 0.3698, Adjusted R-squared: 0.365
F-statistic: 77.03 on 4 and 525 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.564438
Standard error............: 0.035147
Odds ratio (effect size)..: 1.758
Lower 95% CI..............: 1.641
Upper 95% CI..............: 1.884
T-value...................: 16.05932
P-value...................: 1.678039e-47
R^2.......................: 0.369827
Adjusted r^2..............: 0.365025
Sample size of AE DB......: 2423
Sample size of model......: 530
Missing data %............: 78.12629 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines as a function of plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ml_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
392.542541 0.009152 0.248164 -0.196726 -0.242509 0.095364
Med.Statin.LLDyes Med.all.antiplateletyes stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.178209 -0.310615 0.561873 1.110132 0.934465 -0.114191
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.86123 -0.57366 -0.03745 0.44256 2.66670
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.030e+02 1.121e+02 3.594 0.000381 ***
currentDF[, TRAIT] -6.417e-02 5.303e-02 -1.210 0.227275
Age 8.901e-03 6.581e-03 1.353 0.177186
Gendermale 2.308e-01 1.140e-01 2.026 0.043711 *
ORdate_year -2.020e-01 5.595e-02 -3.611 0.000358 ***
Hypertension.compositeyes -9.689e-02 1.563e-01 -0.620 0.535919
DiabetesStatusDiabetes 6.399e-02 1.311e-01 0.488 0.625777
SmokerStatusEx-smoker -2.236e-01 1.163e-01 -1.923 0.055473 .
SmokerStatusNever smoked 1.378e-01 1.836e-01 0.750 0.453757
Med.Statin.LLDyes -2.047e-01 1.163e-01 -1.760 0.079430 .
Med.all.antiplateletyes -3.047e-01 1.923e-01 -1.585 0.114035
GFR_MDRD 1.495e-03 2.969e-03 0.504 0.614915
BMI 2.149e-04 1.562e-02 0.014 0.989033
MedHx_CVDyes 1.169e-01 1.084e-01 1.078 0.281703
stenose50-70% 6.412e-01 9.428e-01 0.680 0.496954
stenose70-90% 1.221e+00 9.000e-01 1.357 0.175799
stenose90-99% 1.045e+00 8.985e-01 1.163 0.245840
stenose100% (Occlusion) 9.608e-03 1.061e+00 0.009 0.992780
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8844 on 295 degrees of freedom
Multiple R-squared: 0.1323, Adjusted R-squared: 0.08225
F-statistic: 2.645 on 17 and 295 DF, p-value: 0.0004977
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.064168
Standard error............: 0.053034
Odds ratio (effect size)..: 0.938
Lower 95% CI..............: 0.845
Upper 95% CI..............: 1.041
T-value...................: -1.209928
P-value...................: 0.227275
R^2.......................: 0.132252
Adjusted r^2..............: 0.082247
Sample size of AE DB......: 2423
Sample size of model......: 313
Missing data %............: 87.08213
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + SmokerStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year SmokerStatusEx-smoker
514.570221 -0.090736 0.008686 0.204460 -0.257261 -0.230470
SmokerStatusNever smoked Med.Statin.LLDyes
0.023567 -0.192415
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.82607 -0.59893 -0.05389 0.47017 2.60324
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 488.844886 120.495720 4.057 6.51e-05 ***
currentDF[, TRAIT] -0.090320 0.055013 -1.642 0.1018
Age 0.007596 0.006748 1.126 0.2613
Gendermale 0.198266 0.118568 1.672 0.0956 .
ORdate_year -0.244476 0.060152 -4.064 6.32e-05 ***
Hypertension.compositeyes -0.091807 0.162354 -0.565 0.5722
DiabetesStatusDiabetes 0.074527 0.133376 0.559 0.5768
SmokerStatusEx-smoker -0.224830 0.117208 -1.918 0.0561 .
SmokerStatusNever smoked 0.088341 0.192119 0.460 0.6460
Med.Statin.LLDyes -0.216649 0.120270 -1.801 0.0728 .
Med.all.antiplateletyes -0.234374 0.200363 -1.170 0.2431
GFR_MDRD 0.000623 0.003205 0.194 0.8460
BMI -0.002501 0.015895 -0.157 0.8751
MedHx_CVDyes 0.124152 0.111365 1.115 0.2659
stenose70-90% 0.508215 0.299639 1.696 0.0910 .
stenose90-99% 0.414293 0.293921 1.410 0.1598
stenose100% (Occlusion) -0.569273 0.615725 -0.925 0.3560
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8698 on 271 degrees of freedom
Multiple R-squared: 0.1381, Adjusted R-squared: 0.08721
F-statistic: 2.714 on 16 and 271 DF, p-value: 0.000502
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.09032
Standard error............: 0.055013
Odds ratio (effect size)..: 0.914
Lower 95% CI..............: 0.82
Upper 95% CI..............: 1.018
T-value...................: -1.641809
P-value...................: 0.1017895
R^2.......................: 0.138099
Adjusted r^2..............: 0.087212
Sample size of AE DB......: 2423
Sample size of model......: 288
Missing data %............: 88.11391
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD +
MedHx_CVD + stenose, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year Med.Statin.LLDyes MedHx_CVDyes stenose70-90%
454.8284 0.2136 -0.2275 -0.2701 0.1515 0.5440
stenose90-99% stenose100% (Occlusion)
0.4301 -0.2911
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8321 -0.5718 -0.0281 0.4503 2.4860
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.794e+02 1.132e+02 4.235 3.07e-05 ***
currentDF[, TRAIT] -5.024e-02 5.133e-02 -0.979 0.3285
Age 8.383e-03 6.514e-03 1.287 0.1991
Gendermale 2.335e-01 1.122e-01 2.082 0.0382 *
ORdate_year -2.398e-01 5.651e-02 -4.244 2.96e-05 ***
Hypertension.compositeyes -1.436e-01 1.526e-01 -0.941 0.3475
DiabetesStatusDiabetes -2.590e-03 1.303e-01 -0.020 0.9842
SmokerStatusEx-smoker -2.053e-01 1.132e-01 -1.814 0.0708 .
SmokerStatusNever smoked 7.838e-02 1.877e-01 0.418 0.6766
Med.Statin.LLDyes -2.196e-01 1.142e-01 -1.923 0.0555 .
Med.all.antiplateletyes -2.461e-01 1.943e-01 -1.267 0.2063
GFR_MDRD 6.336e-04 2.979e-03 0.213 0.8317
BMI 3.290e-03 1.498e-02 0.220 0.8263
MedHx_CVDyes 1.430e-01 1.062e-01 1.347 0.1790
stenose70-90% 4.690e-01 2.921e-01 1.606 0.1094
stenose90-99% 3.491e-01 2.893e-01 1.207 0.2284
stenose100% (Occlusion) -6.651e-01 6.098e-01 -1.091 0.2763
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8636 on 290 degrees of freedom
Multiple R-squared: 0.1351, Adjusted R-squared: 0.08733
F-statistic: 2.83 on 16 and 290 DF, p-value: 0.0002724
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.050244
Standard error............: 0.051331
Odds ratio (effect size)..: 0.951
Lower 95% CI..............: 0.86
Upper 95% CI..............: 1.052
T-value...................: -0.97881
P-value...................: 0.3284897
R^2.......................: 0.135052
Adjusted r^2..............: 0.087331
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + SmokerStatus + Med.all.antiplatelet +
stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year SmokerStatusEx-smoker
536.89076 0.09124 0.01430 0.29158 -0.26864 -0.29724
SmokerStatusNever smoked Med.all.antiplateletyes stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.00165 -0.25853 -0.21202 0.30343 0.13804 -0.79164
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.56234 -0.56044 -0.05685 0.44205 2.87297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.149e+02 1.115e+02 4.618 5.76e-06 ***
currentDF[, TRAIT] 8.383e-02 5.097e-02 1.645 0.1010
Age 1.232e-02 6.825e-03 1.806 0.0720 .
Gendermale 2.905e-01 1.146e-01 2.534 0.0118 *
ORdate_year -2.574e-01 5.561e-02 -4.629 5.49e-06 ***
Hypertension.compositeyes -7.928e-02 1.587e-01 -0.499 0.6178
DiabetesStatusDiabetes 7.003e-02 1.327e-01 0.528 0.5982
SmokerStatusEx-smoker -2.770e-01 1.160e-01 -2.388 0.0176 *
SmokerStatusNever smoked 3.238e-02 1.867e-01 0.173 0.8625
Med.Statin.LLDyes -1.651e-01 1.163e-01 -1.419 0.1570
Med.all.antiplateletyes -2.393e-01 1.840e-01 -1.301 0.1944
GFR_MDRD -6.664e-04 3.069e-03 -0.217 0.8283
BMI -5.253e-03 1.457e-02 -0.360 0.7188
MedHx_CVDyes 8.727e-02 1.080e-01 0.808 0.4199
stenose50-70% -2.776e-01 6.977e-01 -0.398 0.6910
stenose70-90% 2.695e-01 6.507e-01 0.414 0.6790
stenose90-99% 9.198e-02 6.475e-01 0.142 0.8871
stenose100% (Occlusion) -8.701e-01 8.633e-01 -1.008 0.3144
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8929 on 298 degrees of freedom
Multiple R-squared: 0.1513, Adjusted R-squared: 0.1029
F-statistic: 3.126 on 17 and 298 DF, p-value: 4.06e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.083834
Standard error............: 0.050965
Odds ratio (effect size)..: 1.087
Lower 95% CI..............: 0.984
Upper 95% CI..............: 1.202
T-value...................: 1.644913
P-value...................: 0.1010421
R^2.......................: 0.151347
Adjusted r^2..............: 0.102934
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
630.898226 0.294590 0.249463 -0.314914 -0.172490 -0.004928
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0692 -0.4832 -0.1142 0.4492 2.8441
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 633.387067 103.960623 6.093 3.54e-09 ***
currentDF[, TRAIT] 0.278350 0.051053 5.452 1.07e-07 ***
Age 0.001968 0.006444 0.305 0.7603
Gendermale 0.298238 0.110575 2.697 0.0074 **
ORdate_year -0.316106 0.051862 -6.095 3.49e-09 ***
Hypertension.compositeyes -0.089396 0.152294 -0.587 0.5577
DiabetesStatusDiabetes 0.074380 0.127177 0.585 0.5591
SmokerStatusEx-smoker -0.112578 0.109747 -1.026 0.3058
SmokerStatusNever smoked 0.162170 0.183369 0.884 0.3772
Med.Statin.LLDyes -0.183624 0.109619 -1.675 0.0950 .
Med.all.antiplateletyes -0.003682 0.176039 -0.021 0.9833
GFR_MDRD -0.004069 0.002787 -1.460 0.1453
BMI -0.011868 0.013765 -0.862 0.3893
MedHx_CVDyes 0.081132 0.103351 0.785 0.4331
stenose50-70% -0.339190 0.664109 -0.511 0.6099
stenose70-90% 0.156846 0.612840 0.256 0.7982
stenose90-99% 0.008115 0.610594 0.013 0.9894
stenose100% (Occlusion) -0.390377 0.792644 -0.492 0.6227
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8431 on 289 degrees of freedom
Multiple R-squared: 0.2311, Adjusted R-squared: 0.1859
F-statistic: 5.11 on 17 and 289 DF, p-value: 9.142e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.27835
Standard error............: 0.051053
Odds ratio (effect size)..: 1.321
Lower 95% CI..............: 1.195
Upper 95% CI..............: 1.46
T-value...................: 5.452229
P-value...................: 1.067917e-07
R^2.......................: 0.231112
Adjusted r^2..............: 0.185884
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes GFR_MDRD
384.488719 0.093561 0.317187 -0.191987 0.202138 -0.236999 -0.003788
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.86228 -0.56593 -0.09133 0.44977 2.87992
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.976e+02 9.510e+01 4.181 3.72e-05 ***
currentDF[, TRAIT] 8.820e-02 5.159e-02 1.709 0.08832 .
Age -8.335e-04 6.525e-03 -0.128 0.89844
Gendermale 3.226e-01 1.126e-01 2.866 0.00443 **
ORdate_year -1.982e-01 4.745e-02 -4.176 3.80e-05 ***
Hypertension.compositeyes -1.557e-01 1.555e-01 -1.002 0.31720
DiabetesStatusDiabetes 2.314e-01 1.333e-01 1.736 0.08349 .
SmokerStatusEx-smoker -6.849e-02 1.152e-01 -0.595 0.55256
SmokerStatusNever smoked 7.397e-02 1.718e-01 0.431 0.66704
Med.Statin.LLDyes -2.489e-01 1.186e-01 -2.100 0.03652 *
Med.all.antiplateletyes -2.231e-01 1.892e-01 -1.179 0.23912
GFR_MDRD -3.231e-03 2.829e-03 -1.142 0.25419
BMI -1.787e-02 1.389e-02 -1.286 0.19920
MedHx_CVDyes 1.148e-01 1.073e-01 1.069 0.28566
stenose50-70% -2.741e-01 7.202e-01 -0.381 0.70378
stenose70-90% 1.169e-01 6.716e-01 0.174 0.86195
stenose90-99% 3.895e-02 6.685e-01 0.058 0.95357
stenose100% (Occlusion) -8.265e-01 8.305e-01 -0.995 0.32034
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9278 on 328 degrees of freedom
Multiple R-squared: 0.1322, Adjusted R-squared: 0.08727
F-statistic: 2.94 on 17 and 328 DF, p-value: 9.979e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.088196
Standard error............: 0.051594
Odds ratio (effect size)..: 1.092
Lower 95% CI..............: 0.987
Upper 95% CI..............: 1.208
T-value...................: 1.709435
P-value...................: 0.08831622
R^2.......................: 0.132246
Adjusted r^2..............: 0.087271
Sample size of AE DB......: 2423
Sample size of model......: 346
Missing data %............: 85.72018
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + stenose, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
476.6969 0.3218 -0.2382 -0.2260 0.1043 -0.2430
Med.all.antiplateletyes stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.3095 0.4474 0.3865 -0.7015
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.74045 -0.60013 0.00664 0.43651 2.50449
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.916e+02 1.294e+02 3.798 0.000182 ***
currentDF[, TRAIT] -4.521e-02 5.845e-02 -0.773 0.439964
Age 6.254e-03 7.019e-03 0.891 0.373785
Gendermale 3.129e-01 1.224e-01 2.556 0.011150 *
ORdate_year -2.457e-01 6.458e-02 -3.804 0.000177 ***
Hypertension.compositeyes -1.539e-01 1.699e-01 -0.906 0.365744
DiabetesStatusDiabetes 5.404e-02 1.404e-01 0.385 0.700726
SmokerStatusEx-smoker -2.513e-01 1.215e-01 -2.068 0.039665 *
SmokerStatusNever smoked 1.117e-01 1.984e-01 0.563 0.573674
Med.Statin.LLDyes -2.335e-01 1.229e-01 -1.900 0.058569 .
Med.all.antiplateletyes -2.998e-01 2.088e-01 -1.435 0.152359
GFR_MDRD -1.148e-03 3.309e-03 -0.347 0.728890
BMI 2.322e-04 1.607e-02 0.014 0.988481
MedHx_CVDyes 6.599e-02 1.178e-01 0.560 0.575865
stenose70-90% 4.422e-01 3.196e-01 1.384 0.167585
stenose90-99% 3.663e-01 3.147e-01 1.164 0.245494
stenose100% (Occlusion) -7.035e-01 6.363e-01 -1.106 0.269961
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8824 on 258 degrees of freedom
Multiple R-squared: 0.1392, Adjusted R-squared: 0.08582
F-statistic: 2.608 on 16 and 258 DF, p-value: 0.0008597
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.045209
Standard error............: 0.058451
Odds ratio (effect size)..: 0.956
Lower 95% CI..............: 0.852
Upper 95% CI..............: 1.072
T-value...................: -0.773451
P-value...................: 0.4399637
R^2.......................: 0.139206
Adjusted r^2..............: 0.085824
Sample size of AE DB......: 2423
Sample size of model......: 275
Missing data %............: 88.65043
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
SmokerStatus, data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
532.77260 0.01229 0.26064 -0.26656 -0.25418 0.06294
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7378 -0.5775 -0.0569 0.4343 2.5812
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.162e+02 1.229e+02 4.198 3.65e-05 ***
currentDF[, TRAIT] -6.332e-02 5.563e-02 -1.138 0.2561
Age 8.586e-03 6.886e-03 1.247 0.2135
Gendermale 2.577e-01 1.175e-01 2.194 0.0291 *
ORdate_year -2.581e-01 6.135e-02 -4.207 3.53e-05 ***
Hypertension.compositeyes -1.674e-01 1.644e-01 -1.018 0.3097
DiabetesStatusDiabetes 8.757e-02 1.350e-01 0.648 0.5172
SmokerStatusEx-smoker -2.363e-01 1.203e-01 -1.964 0.0506 .
SmokerStatusNever smoked 1.287e-01 1.867e-01 0.689 0.4913
Med.Statin.LLDyes -1.785e-01 1.199e-01 -1.488 0.1379
Med.all.antiplateletyes -2.245e-01 2.009e-01 -1.118 0.2647
GFR_MDRD -1.184e-03 3.189e-03 -0.371 0.7107
BMI -1.466e-04 1.592e-02 -0.009 0.9927
MedHx_CVDyes 7.989e-02 1.126e-01 0.709 0.4788
stenose70-90% 4.777e-01 2.990e-01 1.598 0.1112
stenose90-99% 3.531e-01 2.944e-01 1.199 0.2315
stenose100% (Occlusion) -6.713e-01 7.179e-01 -0.935 0.3506
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8686 on 270 degrees of freedom
Multiple R-squared: 0.1387, Adjusted R-squared: 0.08761
F-statistic: 2.716 on 16 and 270 DF, p-value: 0.0004971
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.063318
Standard error............: 0.055632
Odds ratio (effect size)..: 0.939
Lower 95% CI..............: 0.842
Upper 95% CI..............: 1.047
T-value...................: -1.138172
P-value...................: 0.2560576
R^2.......................: 0.138651
Adjusted r^2..............: 0.087608
Sample size of AE DB......: 2423
Sample size of model......: 287
Missing data %............: 88.15518
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
411.04632 0.10993 0.25064 -0.20526 -0.17670 -0.25620
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.37642 0.14323 0.01004 -0.77295
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.84779 -0.58825 -0.09605 0.49423 2.90353
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 415.427878 90.309630 4.600 5.79e-06 ***
currentDF[, TRAIT] 0.113108 0.048211 2.346 0.01949 *
Age 0.002954 0.005968 0.495 0.62094
Gendermale 0.267744 0.102927 2.601 0.00966 **
ORdate_year -0.207271 0.045061 -4.600 5.79e-06 ***
Hypertension.compositeyes -0.037186 0.139942 -0.266 0.79060
DiabetesStatusDiabetes 0.123337 0.117763 1.047 0.29562
SmokerStatusEx-smoker -0.108876 0.104663 -1.040 0.29889
SmokerStatusNever smoked 0.023734 0.161413 0.147 0.88318
Med.Statin.LLDyes -0.184569 0.108450 -1.702 0.08961 .
Med.all.antiplateletyes -0.224056 0.164980 -1.358 0.17525
GFR_MDRD -0.002470 0.002644 -0.934 0.35086
BMI -0.014774 0.012759 -1.158 0.24763
MedHx_CVDyes 0.077430 0.097591 0.793 0.42804
stenose50-70% -0.374935 0.698676 -0.537 0.59184
stenose70-90% 0.134328 0.657326 0.204 0.83819
stenose90-99% -0.015391 0.655487 -0.023 0.98128
stenose100% (Occlusion) -0.784723 0.809827 -0.969 0.33317
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.909 on 375 degrees of freedom
Multiple R-squared: 0.1231, Adjusted R-squared: 0.08333
F-statistic: 3.096 on 17 and 375 DF, p-value: 3.899e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.113108
Standard error............: 0.048211
Odds ratio (effect size)..: 1.12
Lower 95% CI..............: 1.019
Upper 95% CI..............: 1.231
T-value...................: 2.346125
P-value...................: 0.01948982
R^2.......................: 0.123083
Adjusted r^2..............: 0.083329
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
405.182671 0.104808 0.244204 -0.202331 -0.179626 -0.251704
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.388887 0.136948 0.004196 -0.802007
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.83602 -0.60273 -0.08754 0.48268 2.91794
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 409.015054 90.055759 4.542 7.52e-06 ***
currentDF[, TRAIT] 0.106463 0.048741 2.184 0.0296 *
Age 0.002770 0.005937 0.467 0.6411
Gendermale 0.260558 0.102780 2.535 0.0116 *
ORdate_year -0.204048 0.044933 -4.541 7.54e-06 ***
Hypertension.compositeyes -0.043645 0.139743 -0.312 0.7550
DiabetesStatusDiabetes 0.114297 0.117547 0.972 0.3315
SmokerStatusEx-smoker -0.102813 0.104408 -0.985 0.3254
SmokerStatusNever smoked 0.025817 0.161324 0.160 0.8729
Med.Statin.LLDyes -0.188168 0.108176 -1.739 0.0828 .
Med.all.antiplateletyes -0.220278 0.164886 -1.336 0.1824
GFR_MDRD -0.002499 0.002643 -0.946 0.3449
BMI -0.015318 0.012747 -1.202 0.2302
MedHx_CVDyes 0.077591 0.097267 0.798 0.4255
stenose50-70% -0.388909 0.699121 -0.556 0.5783
stenose70-90% 0.128290 0.657599 0.195 0.8454
stenose90-99% -0.019985 0.655858 -0.030 0.9757
stenose100% (Occlusion) -0.820034 0.809930 -1.012 0.3120
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9088 on 376 degrees of freedom
Multiple R-squared: 0.1213, Adjusted R-squared: 0.0816
F-statistic: 3.054 on 17 and 376 DF, p-value: 4.895e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.106463
Standard error............: 0.048741
Odds ratio (effect size)..: 1.112
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.224
T-value...................: 2.184252
P-value...................: 0.02956092
R^2.......................: 0.121324
Adjusted r^2..............: 0.081597
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
621.99368 -0.09008 0.32069 -0.31052 -0.16285 0.16166
Med.Statin.LLDyes Med.all.antiplateletyes
-0.23576 -0.26283
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.71445 -0.58797 -0.02635 0.45200 2.75578
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 612.052639 117.722658 5.199 3.80e-07 ***
currentDF[, TRAIT] -0.080391 0.060445 -1.330 0.18458
Age 0.005485 0.006683 0.821 0.41247
Gendermale 0.329615 0.116534 2.828 0.00501 **
ORdate_year -0.305579 0.058730 -5.203 3.73e-07 ***
Hypertension.compositeyes -0.120130 0.160966 -0.746 0.45609
DiabetesStatusDiabetes 0.089243 0.131354 0.679 0.49742
SmokerStatusEx-smoker -0.202089 0.115821 -1.745 0.08208 .
SmokerStatusNever smoked 0.181199 0.187965 0.964 0.33585
Med.Statin.LLDyes -0.239536 0.120075 -1.995 0.04700 *
Med.all.antiplateletyes -0.284206 0.183541 -1.548 0.12261
GFR_MDRD -0.001487 0.002996 -0.496 0.61998
BMI -0.008408 0.014307 -0.588 0.55720
MedHx_CVDyes 0.058302 0.111399 0.523 0.60112
stenose50-70% -0.252475 0.707159 -0.357 0.72133
stenose70-90% 0.173170 0.649749 0.267 0.79003
stenose90-99% 0.048313 0.645777 0.075 0.94041
stenose100% (Occlusion) -1.049197 0.928350 -1.130 0.25934
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8866 on 288 degrees of freedom
Multiple R-squared: 0.163, Adjusted R-squared: 0.1136
F-statistic: 3.3 on 17 and 288 DF, p-value: 1.674e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.080391
Standard error............: 0.060445
Odds ratio (effect size)..: 0.923
Lower 95% CI..............: 0.82
Upper 95% CI..............: 1.039
T-value...................: -1.329984
P-value...................: 0.1845759
R^2.......................: 0.163039
Adjusted r^2..............: 0.113635
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year Med.Statin.LLDyes
556.5560 0.2372 -0.2780 -0.1648
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7754 -0.5489 -0.0152 0.4509 2.5088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.433e+02 1.232e+02 4.412 1.50e-05 ***
currentDF[, TRAIT] -3.268e-02 5.481e-02 -0.596 0.5516
Age 7.883e-03 6.839e-03 1.153 0.2501
Gendermale 2.726e-01 1.186e-01 2.298 0.0223 *
ORdate_year -2.718e-01 6.148e-02 -4.420 1.44e-05 ***
Hypertension.compositeyes -1.085e-01 1.638e-01 -0.662 0.5084
DiabetesStatusDiabetes -4.051e-02 1.357e-01 -0.299 0.7655
SmokerStatusEx-smoker -1.902e-01 1.203e-01 -1.582 0.1149
SmokerStatusNever smoked 6.383e-02 1.923e-01 0.332 0.7402
Med.Statin.LLDyes -1.618e-01 1.197e-01 -1.352 0.1776
Med.all.antiplateletyes -1.498e-01 2.050e-01 -0.730 0.4658
GFR_MDRD -5.336e-04 3.171e-03 -0.168 0.8665
BMI 2.839e-03 1.606e-02 0.177 0.8598
MedHx_CVDyes 8.708e-02 1.144e-01 0.761 0.4472
stenose70-90% 4.288e-01 2.998e-01 1.430 0.1539
stenose90-99% 3.476e-01 2.943e-01 1.181 0.2387
stenose100% (Occlusion) -5.880e-01 7.201e-01 -0.817 0.4149
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8656 on 262 degrees of freedom
Multiple R-squared: 0.1332, Adjusted R-squared: 0.08031
F-statistic: 2.517 on 16 and 262 DF, p-value: 0.001306
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.032677
Standard error............: 0.05481
Odds ratio (effect size)..: 0.968
Lower 95% CI..............: 0.869
Upper 95% CI..............: 1.078
T-value...................: -0.596184
P-value...................: 0.5515672
R^2.......................: 0.133239
Adjusted r^2..............: 0.080308
Sample size of AE DB......: 2423
Sample size of model......: 279
Missing data %............: 88.48535
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
327.59797 0.08919 0.25491 -0.16366 -0.18181 -0.25784
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.27839 0.23342 0.09565 -0.73167
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.73867 -0.57017 -0.07035 0.47988 3.01508
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 338.199890 97.550336 3.467 0.000587 ***
currentDF[, TRAIT] 0.087501 0.053520 1.635 0.102901
Age 0.001857 0.005921 0.314 0.753939
Gendermale 0.269380 0.102894 2.618 0.009201 **
ORdate_year -0.168710 0.048674 -3.466 0.000589 ***
Hypertension.compositeyes -0.038802 0.140333 -0.277 0.782315
DiabetesStatusDiabetes 0.129309 0.118727 1.089 0.276795
SmokerStatusEx-smoker -0.070581 0.104977 -0.672 0.501775
SmokerStatusNever smoked 0.079068 0.160842 0.492 0.623297
Med.Statin.LLDyes -0.192336 0.108491 -1.773 0.077065 .
Med.all.antiplateletyes -0.229251 0.165815 -1.383 0.167619
GFR_MDRD -0.002235 0.002668 -0.838 0.402740
BMI -0.017199 0.012801 -1.344 0.179884
MedHx_CVDyes 0.072400 0.097705 0.741 0.459152
stenose50-70% -0.308804 0.699305 -0.442 0.659043
stenose70-90% 0.200661 0.657685 0.305 0.760457
stenose90-99% 0.051597 0.655860 0.079 0.937337
stenose100% (Occlusion) -0.787362 0.811876 -0.970 0.332767
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9113 on 376 degrees of freedom
Multiple R-squared: 0.1165, Adjusted R-squared: 0.07651
F-statistic: 2.915 on 17 and 376 DF, p-value: 0.0001035
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.087501
Standard error............: 0.05352
Odds ratio (effect size)..: 1.091
Lower 95% CI..............: 0.983
Upper 95% CI..............: 1.212
T-value...................: 1.634929
P-value...................: 0.1029007
R^2.......................: 0.116456
Adjusted r^2..............: 0.076509
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
276.033577 0.232392 0.233467 -0.137843 -0.150335 -0.004144
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.05653 -0.61057 -0.04983 0.49520 2.92997
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 294.072490 89.527059 3.285 0.00112 **
currentDF[, TRAIT] 0.229772 0.045925 5.003 8.71e-07 ***
Age 0.002554 0.005791 0.441 0.65949
Gendermale 0.236517 0.100829 2.346 0.01951 *
ORdate_year -0.146876 0.044663 -3.289 0.00110 **
Hypertension.compositeyes -0.014016 0.136176 -0.103 0.91808
DiabetesStatusDiabetes 0.166272 0.115477 1.440 0.15075
SmokerStatusEx-smoker -0.081742 0.101723 -0.804 0.42216
SmokerStatusNever smoked 0.024922 0.156109 0.160 0.87325
Med.Statin.LLDyes -0.162972 0.105611 -1.543 0.12365
Med.all.antiplateletyes -0.180003 0.164625 -1.093 0.27492
GFR_MDRD -0.002865 0.002574 -1.113 0.26635
BMI -0.010551 0.012479 -0.845 0.39840
MedHx_CVDyes 0.057482 0.095203 0.604 0.54635
stenose50-70% -0.170783 0.676686 -0.252 0.80089
stenose70-90% 0.315509 0.635829 0.496 0.62003
stenose90-99% 0.208862 0.633630 0.330 0.74187
stenose100% (Occlusion) -0.388750 0.788899 -0.493 0.62246
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8819 on 372 degrees of freedom
Multiple R-squared: 0.1648, Adjusted R-squared: 0.1266
F-statistic: 4.318 on 17 and 372 DF, p-value: 4.187e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.229772
Standard error............: 0.045925
Odds ratio (effect size)..: 1.258
Lower 95% CI..............: 1.15
Upper 95% CI..............: 1.377
T-value...................: 5.003229
P-value...................: 8.711731e-07
R^2.......................: 0.164813
Adjusted r^2..............: 0.126645
Sample size of AE DB......: 2423
Sample size of model......: 390
Missing data %............: 83.90425
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
390.8669 0.1196 0.2838 -0.1953 0.1815 -0.2409
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.85101 -0.60103 -0.09931 0.46756 2.87469
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.936e+02 9.324e+01 4.221 3.12e-05 ***
currentDF[, TRAIT] 1.233e-01 5.143e-02 2.398 0.01703 *
Age 1.084e-04 6.371e-03 0.017 0.98644
Gendermale 2.983e-01 1.109e-01 2.691 0.00749 **
ORdate_year -1.962e-01 4.653e-02 -4.217 3.17e-05 ***
Hypertension.compositeyes -1.460e-01 1.499e-01 -0.974 0.33074
DiabetesStatusDiabetes 1.943e-01 1.267e-01 1.533 0.12620
SmokerStatusEx-smoker -1.072e-01 1.123e-01 -0.955 0.34044
SmokerStatusNever smoked 3.720e-02 1.686e-01 0.221 0.82549
Med.Statin.LLDyes -2.502e-01 1.158e-01 -2.160 0.03150 *
Med.all.antiplateletyes -1.973e-01 1.841e-01 -1.072 0.28457
GFR_MDRD -2.464e-03 2.747e-03 -0.897 0.37043
BMI -1.472e-02 1.340e-02 -1.099 0.27257
MedHx_CVDyes 9.074e-02 1.049e-01 0.865 0.38748
stenose50-70% -3.553e-01 7.163e-01 -0.496 0.62020
stenose70-90% 3.845e-02 6.682e-01 0.058 0.95415
stenose90-99% -5.637e-02 6.657e-01 -0.085 0.93256
stenose100% (Occlusion) -8.718e-01 8.247e-01 -1.057 0.29121
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9209 on 339 degrees of freedom
Multiple R-squared: 0.1316, Adjusted R-squared: 0.0881
F-statistic: 3.023 on 17 and 339 DF, p-value: 6.264e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.123314
Standard error............: 0.051427
Odds ratio (effect size)..: 1.131
Lower 95% CI..............: 1.023
Upper 95% CI..............: 1.251
T-value...................: 2.397858
P-value...................: 0.0170316
R^2.......................: 0.131645
Adjusted r^2..............: 0.088099
Sample size of AE DB......: 2423
Sample size of model......: 357
Missing data %............: 85.2662
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
305.057176 0.094953 0.262233 -0.152326 -0.181767 -0.004042
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.71821 -0.56090 -0.08476 0.48422 2.96589
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 316.791237 95.783310 3.307 0.00103 **
currentDF[, TRAIT] 0.109248 0.050329 2.171 0.03059 *
Age 0.001929 0.005936 0.325 0.74536
Gendermale 0.262922 0.104073 2.526 0.01194 *
ORdate_year -0.158012 0.047789 -3.306 0.00104 **
Hypertension.compositeyes -0.110746 0.141509 -0.783 0.43436
DiabetesStatusDiabetes 0.170591 0.119654 1.426 0.15480
SmokerStatusEx-smoker -0.058091 0.104846 -0.554 0.57987
SmokerStatusNever smoked 0.080405 0.160410 0.501 0.61650
Med.Statin.LLDyes -0.199272 0.108726 -1.833 0.06764 .
Med.all.antiplateletyes -0.176983 0.169594 -1.044 0.29737
GFR_MDRD -0.003006 0.002650 -1.135 0.25729
BMI -0.017358 0.012826 -1.353 0.17676
MedHx_CVDyes 0.083674 0.098586 0.849 0.39658
stenose50-70% -0.274462 0.698422 -0.393 0.69456
stenose70-90% 0.197684 0.653277 0.303 0.76236
stenose90-99% 0.102251 0.650674 0.157 0.87522
stenose100% (Occlusion) -0.839014 0.809301 -1.037 0.30055
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9056 on 369 degrees of freedom
Multiple R-squared: 0.1196, Adjusted R-squared: 0.07904
F-statistic: 2.949 on 17 and 369 DF, p-value: 8.759e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.109248
Standard error............: 0.050329
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.231
T-value...................: 2.170697
P-value...................: 0.03059111
R^2.......................: 0.119599
Adjusted r^2..............: 0.079039
Sample size of AE DB......: 2423
Sample size of model......: 387
Missing data %............: 84.02806
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD +
Med.all.antiplatelet + stenose, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes stenose50-70%
397.1635 0.2619 -0.1984 -0.1993 -0.2726 -0.2302
stenose70-90% stenose90-99% stenose100% (Occlusion)
0.2540 0.1541 -0.7695
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.83555 -0.57764 -0.07849 0.47187 2.93866
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 433.402151 95.084317 4.558 7.05e-06 ***
currentDF[, TRAIT] 0.057170 0.051539 1.109 0.2680
Age 0.001490 0.006128 0.243 0.8080
Gendermale 0.267166 0.105118 2.542 0.0114 *
ORdate_year -0.216152 0.047444 -4.556 7.12e-06 ***
Hypertension.compositeyes -0.083627 0.143210 -0.584 0.5596
DiabetesStatusDiabetes 0.149945 0.121110 1.238 0.2165
SmokerStatusEx-smoker -0.088808 0.107050 -0.830 0.4073
SmokerStatusNever smoked 0.026311 0.166229 0.158 0.8743
Med.Statin.LLDyes -0.207232 0.110477 -1.876 0.0615 .
Med.all.antiplateletyes -0.251581 0.169125 -1.488 0.1377
GFR_MDRD -0.002510 0.002682 -0.936 0.3500
BMI -0.015311 0.012951 -1.182 0.2379
MedHx_CVDyes 0.050956 0.100224 0.508 0.6115
stenose50-70% -0.332955 0.708961 -0.470 0.6389
stenose70-90% 0.149226 0.663405 0.225 0.8222
stenose90-99% 0.036439 0.661283 0.055 0.9561
stenose100% (Occlusion) -0.870780 0.818548 -1.064 0.2881
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9165 on 365 degrees of freedom
Multiple R-squared: 0.1144, Adjusted R-squared: 0.07314
F-statistic: 2.773 on 17 and 365 DF, p-value: 0.0002245
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.05717
Standard error............: 0.051539
Odds ratio (effect size)..: 1.059
Lower 95% CI..............: 0.957
Upper 95% CI..............: 1.171
T-value...................: 1.109267
P-value...................: 0.2680453
R^2.......................: 0.114389
Adjusted r^2..............: 0.073141
Sample size of AE DB......: 2423
Sample size of model......: 383
Missing data %............: 84.19315
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes MedHx_CVDyes
394.1860 0.1118 0.2846 -0.1970 0.1867 -0.2111 0.1530
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.80479 -0.55718 -0.07259 0.49249 2.92738
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 392.264600 93.612243 4.190 3.58e-05 ***
currentDF[, TRAIT] 0.114320 0.050694 2.255 0.02478 *
Age 0.001696 0.006430 0.264 0.79215
Gendermale 0.291645 0.109263 2.669 0.00798 **
ORdate_year -0.195688 0.046707 -4.190 3.58e-05 ***
Hypertension.compositeyes -0.096244 0.148954 -0.646 0.51864
DiabetesStatusDiabetes 0.198566 0.127435 1.558 0.12014
SmokerStatusEx-smoker -0.092502 0.112632 -0.821 0.41208
SmokerStatusNever smoked -0.060865 0.175315 -0.347 0.72868
Med.Statin.LLDyes -0.221663 0.114899 -1.929 0.05456 .
Med.all.antiplateletyes -0.178895 0.173723 -1.030 0.30387
GFR_MDRD -0.002547 0.002792 -0.912 0.36224
BMI -0.015900 0.013497 -1.178 0.23962
MedHx_CVDyes 0.150332 0.104125 1.444 0.14975
stenose50-70% -0.328698 0.704867 -0.466 0.64129
stenose70-90% 0.119639 0.660407 0.181 0.85635
stenose90-99% 0.012923 0.657247 0.020 0.98432
stenose100% (Occlusion) -0.687557 0.815708 -0.843 0.39989
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9122 on 332 degrees of freedom
Multiple R-squared: 0.1314, Adjusted R-squared: 0.08696
F-statistic: 2.955 on 17 and 332 DF, p-value: 9.137e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.11432
Standard error............: 0.050694
Odds ratio (effect size)..: 1.121
Lower 95% CI..............: 1.015
Upper 95% CI..............: 1.238
T-value...................: 2.255113
P-value...................: 0.02477717
R^2.......................: 0.131437
Adjusted r^2..............: 0.086963
Sample size of AE DB......: 2423
Sample size of model......: 350
Missing data %............: 85.5551
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
414.66554 0.08220 0.24220 -0.20708 -0.17596 -0.24872
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.36331 0.16201 0.03217 -0.80655
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.87499 -0.58734 -0.07708 0.44780 2.97087
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 417.498390 90.931970 4.591 6.02e-06 ***
currentDF[, TRAIT] 0.080481 0.050107 1.606 0.1091
Age 0.002049 0.005935 0.345 0.7301
Gendermale 0.260287 0.103599 2.512 0.0124 *
ORdate_year -0.208268 0.045369 -4.591 6.04e-06 ***
Hypertension.compositeyes -0.050677 0.140089 -0.362 0.7177
DiabetesStatusDiabetes 0.110263 0.117855 0.936 0.3501
SmokerStatusEx-smoker -0.099123 0.104723 -0.947 0.3445
SmokerStatusNever smoked 0.033779 0.162004 0.209 0.8349
Med.Statin.LLDyes -0.185243 0.108516 -1.707 0.0886 .
Med.all.antiplateletyes -0.218136 0.165403 -1.319 0.1880
GFR_MDRD -0.002504 0.002653 -0.944 0.3458
BMI -0.015520 0.012784 -1.214 0.2255
MedHx_CVDyes 0.077145 0.097579 0.791 0.4297
stenose50-70% -0.364847 0.701528 -0.520 0.6033
stenose70-90% 0.154109 0.659930 0.234 0.8155
stenose90-99% 0.010521 0.658143 0.016 0.9873
stenose100% (Occlusion) -0.828383 0.812917 -1.019 0.3088
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9114 on 376 degrees of freedom
Multiple R-squared: 0.1162, Adjusted R-squared: 0.07628
F-statistic: 2.909 on 17 and 376 DF, p-value: 0.0001069
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.080481
Standard error............: 0.050107
Odds ratio (effect size)..: 1.084
Lower 95% CI..............: 0.982
Upper 95% CI..............: 1.196
T-value...................: 1.606182
P-value...................: 0.1090734
R^2.......................: 0.116239
Adjusted r^2..............: 0.076281
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes Med.Statin.LLDyes
237.723792 0.128988 0.237129 -0.118596 -0.202177 -0.233406
GFR_MDRD
-0.004844
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7881 -0.5603 -0.1068 0.4896 2.7203
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.632e+02 1.113e+02 2.364 0.0187 *
currentDF[, TRAIT] 1.240e-01 5.316e-02 2.333 0.0203 *
Age 1.261e-04 6.320e-03 0.020 0.9841
Gendermale 2.380e-01 1.104e-01 2.156 0.0318 *
ORdate_year -1.310e-01 5.556e-02 -2.358 0.0190 *
Hypertension.compositeyes -2.000e-01 1.487e-01 -1.345 0.1796
DiabetesStatusDiabetes 1.425e-01 1.279e-01 1.114 0.2662
SmokerStatusEx-smoker -3.534e-02 1.133e-01 -0.312 0.7553
SmokerStatusNever smoked 6.836e-02 1.678e-01 0.408 0.6839
Med.Statin.LLDyes -2.679e-01 1.194e-01 -2.243 0.0256 *
Med.all.antiplateletyes -1.227e-01 1.787e-01 -0.687 0.4928
GFR_MDRD -4.296e-03 2.912e-03 -1.475 0.1412
BMI -2.046e-02 1.398e-02 -1.464 0.1443
MedHx_CVDyes 3.161e-02 1.064e-01 0.297 0.7667
stenose50-70% -3.162e-01 7.028e-01 -0.450 0.6531
stenose70-90% 1.214e-01 6.552e-01 0.185 0.8531
stenose90-99% 1.514e-02 6.531e-01 0.023 0.9815
stenose100% (Occlusion) -7.670e-01 8.121e-01 -0.945 0.3456
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9061 on 318 degrees of freedom
Multiple R-squared: 0.108, Adjusted R-squared: 0.06026
F-statistic: 2.264 on 17 and 318 DF, p-value: 0.00319
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.124034
Standard error............: 0.053158
Odds ratio (effect size)..: 1.132
Lower 95% CI..............: 1.02
Upper 95% CI..............: 1.256
T-value...................: 2.333299
P-value...................: 0.02025553
R^2.......................: 0.107952
Adjusted r^2..............: 0.060264
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes
312.9352 0.1300 0.2676 -0.1564 -0.1703
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.76275 -0.56777 -0.06522 0.49499 3.06302
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 334.699245 94.671396 3.535 0.000458 ***
currentDF[, TRAIT] 0.109715 0.050424 2.176 0.030190 *
Age 0.001597 0.005887 0.271 0.786364
Gendermale 0.282247 0.102195 2.762 0.006030 **
ORdate_year -0.167046 0.047225 -3.537 0.000455 ***
Hypertension.compositeyes -0.022396 0.140390 -0.160 0.873339
DiabetesStatusDiabetes 0.130251 0.118046 1.103 0.270563
SmokerStatusEx-smoker -0.083711 0.104194 -0.803 0.422246
SmokerStatusNever smoked 0.053518 0.160351 0.334 0.738749
Med.Statin.LLDyes -0.191845 0.108187 -1.773 0.076993 .
Med.all.antiplateletyes -0.182511 0.165129 -1.105 0.269751
GFR_MDRD -0.002411 0.002645 -0.911 0.362630
BMI -0.015971 0.012744 -1.253 0.210910
MedHx_CVDyes 0.074556 0.097314 0.766 0.444079
stenose50-70% -0.169455 0.698458 -0.243 0.808437
stenose70-90% 0.300234 0.655380 0.458 0.647140
stenose90-99% 0.157672 0.652982 0.241 0.809327
stenose100% (Occlusion) -0.581800 0.813639 -0.715 0.475017
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9088 on 376 degrees of freedom
Multiple R-squared: 0.1212, Adjusted R-squared: 0.08151
F-statistic: 3.051 on 17 and 376 DF, p-value: 4.959e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.109715
Standard error............: 0.050424
Odds ratio (effect size)..: 1.116
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.232
T-value...................: 2.175832
P-value...................: 0.03018994
R^2.......................: 0.12124
Adjusted r^2..............: 0.081508
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
348.14977 0.08718 0.31526 -0.17399 0.20694 -0.24560
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.82919 -0.56626 -0.07603 0.44307 2.90328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 358.629055 99.358952 3.609 0.000354 ***
currentDF[, TRAIT] 0.073564 0.054208 1.357 0.175673
Age -0.001779 0.006266 -0.284 0.776669
Gendermale 0.326305 0.110626 2.950 0.003405 **
ORdate_year -0.178688 0.049575 -3.604 0.000360 ***
Hypertension.compositeyes -0.147400 0.150938 -0.977 0.329491
DiabetesStatusDiabetes 0.222332 0.128788 1.726 0.085205 .
SmokerStatusEx-smoker -0.060232 0.111996 -0.538 0.591069
SmokerStatusNever smoked 0.074090 0.168858 0.439 0.661109
Med.Statin.LLDyes -0.260920 0.115344 -2.262 0.024330 *
Med.all.antiplateletyes -0.214591 0.187904 -1.142 0.254257
GFR_MDRD -0.003189 0.002781 -1.147 0.252316
BMI -0.018247 0.013473 -1.354 0.176535
MedHx_CVDyes 0.109407 0.105124 1.041 0.298741
stenose50-70% -0.265247 0.714856 -0.371 0.710834
stenose70-90% 0.116664 0.666864 0.175 0.861229
stenose90-99% 0.032181 0.664416 0.048 0.961399
stenose100% (Occlusion) -0.846591 0.824170 -1.027 0.305063
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.921 on 336 degrees of freedom
Multiple R-squared: 0.1307, Adjusted R-squared: 0.08671
F-statistic: 2.972 on 17 and 336 DF, p-value: 8.305e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.073564
Standard error............: 0.054208
Odds ratio (effect size)..: 1.076
Lower 95% CI..............: 0.968
Upper 95% CI..............: 1.197
T-value...................: 1.357061
P-value...................: 0.1756727
R^2.......................: 0.130697
Adjusted r^2..............: 0.086715
Sample size of AE DB......: 2423
Sample size of model......: 354
Missing data %............: 85.39001
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
387.557181 0.176005 0.228372 -0.193386 -0.155389 -0.255899
GFR_MDRD
-0.003847
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.60973 -0.58187 -0.07439 0.49164 2.76577
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 398.165295 89.034156 4.472 1.03e-05 ***
currentDF[, TRAIT] 0.171332 0.045934 3.730 0.000221 ***
Age 0.002770 0.005859 0.473 0.636586
Gendermale 0.249260 0.101988 2.444 0.014985 *
ORdate_year -0.198710 0.044423 -4.473 1.02e-05 ***
Hypertension.compositeyes -0.007183 0.138807 -0.052 0.958757
DiabetesStatusDiabetes 0.140120 0.116610 1.202 0.230274
SmokerStatusEx-smoker -0.117795 0.103470 -1.138 0.255664
SmokerStatusNever smoked 0.024242 0.159003 0.152 0.878904
Med.Statin.LLDyes -0.179756 0.107285 -1.676 0.094669 .
Med.all.antiplateletyes -0.250028 0.163436 -1.530 0.126904
GFR_MDRD -0.002801 0.002612 -1.073 0.284179
BMI -0.013525 0.012627 -1.071 0.284821
MedHx_CVDyes 0.078918 0.096497 0.818 0.413974
stenose50-70% -0.242528 0.689611 -0.352 0.725270
stenose70-90% 0.247590 0.648124 0.382 0.702671
stenose90-99% 0.103883 0.645828 0.161 0.872296
stenose100% (Occlusion) -0.563330 0.802636 -0.702 0.483208
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8992 on 375 degrees of freedom
Multiple R-squared: 0.142, Adjusted R-squared: 0.1031
F-statistic: 3.652 on 17 and 375 DF, p-value: 1.799e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.171332
Standard error............: 0.045934
Odds ratio (effect size)..: 1.187
Lower 95% CI..............: 1.085
Upper 95% CI..............: 1.299
T-value...................: 3.729993
P-value...................: 0.0002210852
R^2.......................: 0.142042
Adjusted r^2..............: 0.103148
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
330.96847 0.13643 0.26079 -0.16535 -0.16932 -0.23566
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.30981 0.21930 0.08342 -0.66366
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.77943 -0.59964 -0.04075 0.47551 2.95561
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 339.289933 91.849462 3.694 0.000254 ***
currentDF[, TRAIT] 0.138783 0.047865 2.899 0.003957 **
Age 0.003679 0.005926 0.621 0.535045
Gendermale 0.273430 0.101774 2.687 0.007538 **
ORdate_year -0.169320 0.045823 -3.695 0.000252 ***
Hypertension.compositeyes -0.047009 0.139031 -0.338 0.735460
DiabetesStatusDiabetes 0.133514 0.117326 1.138 0.255855
SmokerStatusEx-smoker -0.084940 0.103684 -0.819 0.413184
SmokerStatusNever smoked 0.037356 0.159790 0.234 0.815280
Med.Statin.LLDyes -0.175236 0.107771 -1.626 0.104786
Med.all.antiplateletyes -0.204007 0.163981 -1.244 0.214242
GFR_MDRD -0.002214 0.002635 -0.840 0.401198
BMI -0.016811 0.012687 -1.325 0.185943
MedHx_CVDyes 0.057699 0.097157 0.594 0.552957
stenose50-70% -0.334540 0.694012 -0.482 0.630060
stenose70-90% 0.188147 0.652329 0.288 0.773182
stenose90-99% 0.039120 0.650224 0.060 0.952057
stenose100% (Occlusion) -0.709359 0.805817 -0.880 0.379260
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9045 on 376 degrees of freedom
Multiple R-squared: 0.1296, Adjusted R-squared: 0.09028
F-statistic: 3.294 on 17 and 376 DF, p-value: 1.314e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.138783
Standard error............: 0.047865
Odds ratio (effect size)..: 1.149
Lower 95% CI..............: 1.046
Upper 95% CI..............: 1.262
T-value...................: 2.899477
P-value...................: 0.003957249
R^2.......................: 0.129635
Adjusted r^2..............: 0.090284
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.all.antiplatelet + GFR_MDRD + BMI + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.all.antiplateletyes GFR_MDRD
605.44365 0.21623 0.27216 -0.30240 -0.27935 -0.00511
BMI stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.02159 0.80800 1.23457 1.03777 0.19344
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6355 -0.5328 -0.1320 0.4369 2.7713
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 603.371833 102.046952 5.913 8.83e-09 ***
currentDF[, TRAIT] 0.204483 0.049258 4.151 4.27e-05 ***
Age 0.001610 0.006012 0.268 0.7890
Gendermale 0.298705 0.104647 2.854 0.0046 **
ORdate_year -0.301318 0.050931 -5.916 8.66e-09 ***
Hypertension.compositeyes -0.163890 0.145500 -1.126 0.2609
DiabetesStatusDiabetes -0.013334 0.117617 -0.113 0.9098
SmokerStatusEx-smoker -0.087816 0.105082 -0.836 0.4040
SmokerStatusNever smoked 0.158093 0.167713 0.943 0.3466
Med.Statin.LLDyes -0.072908 0.108889 -0.670 0.5036
Med.all.antiplateletyes -0.263286 0.158782 -1.658 0.0983 .
GFR_MDRD -0.005346 0.002544 -2.102 0.0364 *
BMI -0.020340 0.013165 -1.545 0.1233
MedHx_CVDyes 0.050893 0.099678 0.511 0.6100
stenose50-70% 0.735314 0.889422 0.827 0.4090
stenose70-90% 1.178954 0.852140 1.384 0.1675
stenose90-99% 0.965463 0.850819 1.135 0.2574
stenose100% (Occlusion) 0.087200 0.959619 0.091 0.9277
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8426 on 312 degrees of freedom
Multiple R-squared: 0.2061, Adjusted R-squared: 0.1628
F-statistic: 4.764 on 17 and 312 DF, p-value: 4.949e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.204483
Standard error............: 0.049258
Odds ratio (effect size)..: 1.227
Lower 95% CI..............: 1.114
Upper 95% CI..............: 1.351
T-value...................: 4.151285
P-value...................: 4.270246e-05
R^2.......................: 0.206079
Adjusted r^2..............: 0.162821
Sample size of AE DB......: 2423
Sample size of model......: 330
Missing data %............: 86.38052
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year GFR_MDRD
422.567714 0.245472 -0.211023 -0.004055
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.75331 -0.60293 -0.06849 0.46663 3.03503
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 397.931460 94.466998 4.212 3.19e-05 ***
currentDF[, TRAIT] 0.060049 0.048611 1.235 0.2175
Age 0.000681 0.006075 0.112 0.9108
Gendermale 0.271881 0.105766 2.571 0.0105 *
ORdate_year -0.198487 0.047134 -4.211 3.20e-05 ***
Hypertension.compositeyes -0.074295 0.142203 -0.522 0.6017
DiabetesStatusDiabetes 0.108392 0.119629 0.906 0.3655
SmokerStatusEx-smoker -0.077703 0.106553 -0.729 0.4663
SmokerStatusNever smoked 0.143648 0.165490 0.868 0.3860
Med.Statin.LLDyes -0.159324 0.110967 -1.436 0.1519
Med.all.antiplateletyes -0.189435 0.169337 -1.119 0.2640
GFR_MDRD -0.003119 0.002710 -1.151 0.2506
BMI -0.015967 0.013179 -1.212 0.2265
MedHx_CVDyes 0.077823 0.099817 0.780 0.4361
stenose50-70% -0.325158 0.703195 -0.462 0.6441
stenose70-90% 0.216771 0.663164 0.327 0.7439
stenose90-99% 0.091778 0.660753 0.139 0.8896
stenose100% (Occlusion) -0.813254 0.819977 -0.992 0.3219
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.919 on 366 degrees of freedom
Multiple R-squared: 0.1153, Adjusted R-squared: 0.07423
F-statistic: 2.806 on 17 and 366 DF, p-value: 0.000188
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.060049
Standard error............: 0.048611
Odds ratio (effect size)..: 1.062
Lower 95% CI..............: 0.965
Upper 95% CI..............: 1.168
T-value...................: 1.235294
P-value...................: 0.2175134
R^2.......................: 0.11532
Adjusted r^2..............: 0.074228
Sample size of AE DB......: 2423
Sample size of model......: 384
Missing data %............: 84.15188
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
488.244559 0.075016 0.316277 -0.243790 -0.160502 -0.003945
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8689 -0.5762 -0.0745 0.4982 3.2666
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.951e+02 9.547e+01 5.186 3.54e-07 ***
currentDF[, TRAIT] 6.707e-02 5.122e-02 1.309 0.19125
Age -8.326e-04 5.900e-03 -0.141 0.88785
Gendermale 3.346e-01 1.030e-01 3.250 0.00126 **
ORdate_year -2.469e-01 4.763e-02 -5.185 3.57e-07 ***
Hypertension.compositeyes -4.015e-02 1.437e-01 -0.279 0.78010
DiabetesStatusDiabetes 5.294e-02 1.166e-01 0.454 0.65017
SmokerStatusEx-smoker -8.013e-02 1.043e-01 -0.768 0.44288
SmokerStatusNever smoked 1.326e-01 1.611e-01 0.823 0.41093
Med.Statin.LLDyes -1.705e-01 1.071e-01 -1.592 0.11228
Med.all.antiplateletyes -2.210e-01 1.689e-01 -1.309 0.19140
GFR_MDRD -3.499e-03 2.617e-03 -1.337 0.18207
BMI -1.624e-02 1.301e-02 -1.248 0.21285
MedHx_CVDyes 8.124e-02 9.803e-02 0.829 0.40776
stenose50-70% -2.629e-01 6.925e-01 -0.380 0.70438
stenose70-90% 1.794e-01 6.541e-01 0.274 0.78401
stenose90-99% 6.799e-02 6.515e-01 0.104 0.91695
stenose100% (Occlusion) -8.184e-01 8.078e-01 -1.013 0.31168
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9055 on 371 degrees of freedom
Multiple R-squared: 0.1412, Adjusted R-squared: 0.1018
F-statistic: 3.588 on 17 and 371 DF, p-value: 2.607e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.067066
Standard error............: 0.051224
Odds ratio (effect size)..: 1.069
Lower 95% CI..............: 0.967
Upper 95% CI..............: 1.182
T-value...................: 1.309275
P-value...................: 0.1912516
R^2.......................: 0.141198
Adjusted r^2..............: 0.101845
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + BMI + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes BMI
518.11414 0.22361 0.20944 -0.25837 -0.16145 -0.02197
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.68467 -0.21566 -0.27056 -1.16796
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06404 -0.54875 -0.05214 0.45277 3.05284
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 528.162165 92.488230 5.711 2.31e-08 ***
currentDF[, TRAIT] 0.217754 0.048550 4.485 9.73e-06 ***
Age -0.001146 0.005728 -0.200 0.8415
Gendermale 0.232349 0.102014 2.278 0.0233 *
ORdate_year -0.263143 0.046144 -5.703 2.41e-08 ***
Hypertension.compositeyes -0.101608 0.138007 -0.736 0.4620
DiabetesStatusDiabetes 0.101299 0.114406 0.885 0.3765
SmokerStatusEx-smoker -0.039920 0.101825 -0.392 0.6952
SmokerStatusNever smoked 0.189772 0.156136 1.215 0.2250
Med.Statin.LLDyes -0.156760 0.104590 -1.499 0.1348
Med.all.antiplateletyes -0.155969 0.164814 -0.946 0.3446
GFR_MDRD -0.002493 0.002562 -0.973 0.3313
BMI -0.024847 0.012805 -1.940 0.0531 .
MedHx_CVDyes 0.076311 0.095565 0.799 0.4251
stenose50-70% -0.663006 0.680455 -0.974 0.3305
stenose70-90% -0.237089 0.644417 -0.368 0.7131
stenose90-99% -0.290772 0.640210 -0.454 0.6500
stenose100% (Occlusion) -1.290942 0.795104 -1.624 0.1053
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8839 on 371 degrees of freedom
Multiple R-squared: 0.1816, Adjusted R-squared: 0.1441
F-statistic: 4.843 on 17 and 371 DF, p-value: 2.095e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.217754
Standard error............: 0.04855
Odds ratio (effect size)..: 1.243
Lower 95% CI..............: 1.13
Upper 95% CI..............: 1.367
T-value...................: 4.485114
P-value...................: 9.730762e-06
R^2.......................: 0.181604
Adjusted r^2..............: 0.144104
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + BMI +
stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
484.54051 0.12798 0.25522 -0.24170 -0.16630 -0.23587
BMI stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.01882 -0.37517 0.08306 -0.01948 -0.95485
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.92719 -0.56131 -0.06458 0.43887 3.07875
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 485.689729 94.610884 5.134 4.6e-07 ***
currentDF[, TRAIT] 0.119163 0.046927 2.539 0.01151 *
Age -0.001413 0.005830 -0.242 0.80859
Gendermale 0.287381 0.102738 2.797 0.00542 **
ORdate_year -0.242140 0.047207 -5.129 4.7e-07 ***
Hypertension.compositeyes -0.049814 0.140702 -0.354 0.72351
DiabetesStatusDiabetes 0.074169 0.116263 0.638 0.52391
SmokerStatusEx-smoker -0.060501 0.103495 -0.585 0.55919
SmokerStatusNever smoked 0.147501 0.158853 0.929 0.35373
Med.Statin.LLDyes -0.174343 0.106397 -1.639 0.10214
Med.all.antiplateletyes -0.207719 0.167400 -1.241 0.21544
GFR_MDRD -0.002866 0.002617 -1.095 0.27418
BMI -0.020951 0.013005 -1.611 0.10803
MedHx_CVDyes 0.088529 0.097239 0.910 0.36319
stenose50-70% -0.322255 0.687739 -0.469 0.63965
stenose70-90% 0.113538 0.650027 0.175 0.86144
stenose90-99% -0.002686 0.647492 -0.004 0.99669
stenose100% (Occlusion) -0.905485 0.803171 -1.127 0.26031
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8998 on 371 degrees of freedom
Multiple R-squared: 0.152, Adjusted R-squared: 0.1131
F-statistic: 3.911 on 17 and 371 DF, p-value: 4.25e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.119163
Standard error............: 0.046927
Odds ratio (effect size)..: 1.127
Lower 95% CI..............: 1.028
Upper 95% CI..............: 1.235
T-value...................: 2.539309
P-value...................: 0.01151472
R^2.......................: 0.151969
Adjusted r^2..............: 0.11311
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
Analysis of MCP1_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
337.77686 -0.07302 0.22973 -0.16864
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3564 -0.6994 -0.0096 0.6442 2.3502
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 324.135124 114.029542 2.843 0.00473 **
currentDF[, TRAIT] -0.091842 0.052701 -1.743 0.08225 .
Age -0.010907 0.006841 -1.594 0.11173
Gendermale 0.280439 0.121542 2.307 0.02161 *
ORdate_year -0.161011 0.056903 -2.830 0.00493 **
Hypertension.compositeyes -0.222927 0.155898 -1.430 0.15361
DiabetesStatusDiabetes -0.112047 0.138121 -0.811 0.41778
SmokerStatusEx-smoker 0.057259 0.119725 0.478 0.63276
SmokerStatusNever smoked 0.369203 0.179991 2.051 0.04098 *
Med.Statin.LLDyes -0.168555 0.119333 -1.412 0.15869
Med.all.antiplateletyes 0.234563 0.205958 1.139 0.25552
GFR_MDRD -0.002512 0.003084 -0.815 0.41586
BMI -0.007833 0.015379 -0.509 0.61084
MedHx_CVDyes 0.042501 0.110771 0.384 0.70144
stenose50-70% -0.622075 0.779274 -0.798 0.42525
stenose70-90% -0.509955 0.717059 -0.711 0.47744
stenose90-99% -0.556706 0.715558 -0.778 0.43709
stenose100% (Occlusion) -0.781108 0.887784 -0.880 0.37954
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9891 on 354 degrees of freedom
Multiple R-squared: 0.06962, Adjusted R-squared: 0.02494
F-statistic: 1.558 on 17 and 354 DF, p-value: 0.073
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.091842
Standard error............: 0.052701
Odds ratio (effect size)..: 0.912
Lower 95% CI..............: 0.823
Upper 95% CI..............: 1.012
T-value...................: -1.742702
P-value...................: 0.08225396
R^2.......................: 0.069619
Adjusted r^2..............: 0.02494
Sample size of AE DB......: 2423
Sample size of model......: 372
Missing data %............: 84.64713
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
391.3396 0.3126 -0.1954
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3067 -0.7046 0.0354 0.6417 2.5773
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 366.184851 124.633598 2.938 0.00354 **
currentDF[, TRAIT] -0.050504 0.056698 -0.891 0.37371
Age -0.013894 0.007279 -1.909 0.05715 .
Gendermale 0.354830 0.129438 2.741 0.00646 **
ORdate_year -0.182242 0.062216 -2.929 0.00364 **
Hypertension.compositeyes -0.112361 0.165304 -0.680 0.49716
DiabetesStatusDiabetes -0.118886 0.145318 -0.818 0.41389
SmokerStatusEx-smoker 0.089026 0.124760 0.714 0.47600
SmokerStatusNever smoked 0.341531 0.190332 1.794 0.07368 .
Med.Statin.LLDyes -0.156028 0.126548 -1.233 0.21848
Med.all.antiplateletyes 0.158071 0.219711 0.719 0.47238
GFR_MDRD -0.003372 0.003474 -0.971 0.33234
BMI -0.003079 0.016501 -0.187 0.85209
MedHx_CVDyes 0.061412 0.117000 0.525 0.60002
stenose50-70% -0.093615 1.077451 -0.087 0.93082
stenose70-90% -0.040576 1.030674 -0.039 0.96862
stenose90-99% -0.058983 1.027499 -0.057 0.95426
stenose100% (Occlusion) -0.257200 1.161929 -0.221 0.82495
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.001 on 325 degrees of freedom
Multiple R-squared: 0.07236, Adjusted R-squared: 0.02384
F-statistic: 1.491 on 17 and 325 DF, p-value: 0.09541
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.050504
Standard error............: 0.056698
Odds ratio (effect size)..: 0.951
Lower 95% CI..............: 0.851
Upper 95% CI..............: 1.062
T-value...................: -0.89077
P-value...................: 0.3737117
R^2.......................: 0.072361
Adjusted r^2..............: 0.023838
Sample size of AE DB......: 2423
Sample size of model......: 343
Missing data %............: 85.844
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Med.Statin.LLDyes
348.44629 -0.11317 -0.01065 0.29540 -0.17354 -0.21941
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5069 -0.7021 0.0220 0.6314 2.2770
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 346.815626 117.537234 2.951 0.00339 **
currentDF[, TRAIT] -0.116354 0.053150 -2.189 0.02925 *
Age -0.015717 0.006997 -2.246 0.02532 *
Gendermale 0.344607 0.121863 2.828 0.00496 **
ORdate_year -0.172428 0.058668 -2.939 0.00351 **
Hypertension.compositeyes -0.111181 0.155967 -0.713 0.47642
DiabetesStatusDiabetes -0.141743 0.140027 -1.012 0.31213
SmokerStatusEx-smoker 0.073269 0.119671 0.612 0.54077
SmokerStatusNever smoked 0.313783 0.185013 1.696 0.09078 .
Med.Statin.LLDyes -0.235154 0.120323 -1.954 0.05146 .
Med.all.antiplateletyes 0.160528 0.211803 0.758 0.44902
GFR_MDRD -0.003817 0.003191 -1.196 0.23245
BMI -0.004306 0.015178 -0.284 0.77683
MedHx_CVDyes 0.092628 0.111450 0.831 0.40648
stenose50-70% -0.046230 1.060366 -0.044 0.96525
stenose70-90% -0.051028 1.012527 -0.050 0.95983
stenose90-99% -0.077836 1.010546 -0.077 0.93865
stenose100% (Occlusion) -0.339590 1.141656 -0.297 0.76630
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9872 on 346 degrees of freedom
Multiple R-squared: 0.08442, Adjusted R-squared: 0.03944
F-statistic: 1.877 on 17 and 346 DF, p-value: 0.01904
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.116354
Standard error............: 0.05315
Odds ratio (effect size)..: 0.89
Lower 95% CI..............: 0.802
Upper 95% CI..............: 0.988
T-value...................: -2.189154
P-value...................: 0.02925333
R^2.......................: 0.084423
Adjusted r^2..............: 0.039438
Sample size of AE DB......: 2423
Sample size of model......: 364
Missing data %............: 84.9773
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
303.5723 0.2429 -0.1516
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1707 -0.6816 -0.0241 0.6683 2.4450
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 292.598955 112.086171 2.610 0.00942 **
currentDF[, TRAIT] 0.051411 0.051375 1.001 0.31764
Age -0.014239 0.007037 -2.023 0.04375 *
Gendermale 0.310939 0.121720 2.555 0.01104 *
ORdate_year -0.145267 0.055919 -2.598 0.00976 **
Hypertension.compositeyes -0.143512 0.156731 -0.916 0.36045
DiabetesStatusDiabetes -0.081443 0.137404 -0.593 0.55373
SmokerStatusEx-smoker 0.098193 0.118217 0.831 0.40673
SmokerStatusNever smoked 0.387337 0.180654 2.144 0.03269 *
Med.Statin.LLDyes -0.156889 0.118637 -1.322 0.18685
Med.all.antiplateletyes 0.157364 0.191973 0.820 0.41291
GFR_MDRD -0.003912 0.003160 -1.238 0.21647
BMI -0.012374 0.014417 -0.858 0.39130
MedHx_CVDyes 0.029785 0.109981 0.271 0.78669
stenose50-70% -0.238627 0.655442 -0.364 0.71602
stenose70-90% -0.088126 0.597638 -0.147 0.88285
stenose90-99% -0.162556 0.595502 -0.273 0.78503
stenose100% (Occlusion) -0.411221 0.797116 -0.516 0.60625
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.001 on 364 degrees of freedom
Multiple R-squared: 0.06287, Adjusted R-squared: 0.01911
F-statistic: 1.437 on 17 and 364 DF, p-value: 0.1162
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.051411
Standard error............: 0.051375
Odds ratio (effect size)..: 1.053
Lower 95% CI..............: 0.952
Upper 95% CI..............: 1.164
T-value...................: 1.00069
P-value...................: 0.3176413
R^2.......................: 0.062873
Adjusted r^2..............: 0.019106
Sample size of AE DB......: 2423
Sample size of model......: 382
Missing data %............: 84.23442
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + SmokerStatus +
Med.all.antiplatelet + GFR_MDRD + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Hypertension.compositeyes
483.713537 0.324434 -0.018361 0.307909 -0.240502 -0.266232
SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes GFR_MDRD BMI
0.103620 0.407957 0.324185 -0.004823 -0.020470
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2578 -0.6013 -0.0235 0.5901 2.3891
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 468.385552 104.233897 4.494 9.53e-06 ***
currentDF[, TRAIT] 0.323207 0.051592 6.265 1.10e-09 ***
Age -0.018659 0.006544 -2.851 0.00461 **
Gendermale 0.316040 0.113877 2.775 0.00581 **
ORdate_year -0.232817 0.052003 -4.477 1.03e-05 ***
Hypertension.compositeyes -0.225756 0.148680 -1.518 0.12981
DiabetesStatusDiabetes -0.160255 0.129844 -1.234 0.21795
SmokerStatusEx-smoker 0.106100 0.110630 0.959 0.33819
SmokerStatusNever smoked 0.427245 0.175653 2.432 0.01550 *
Med.Statin.LLDyes -0.099350 0.110753 -0.897 0.37032
Med.all.antiplateletyes 0.292253 0.175618 1.664 0.09698 .
GFR_MDRD -0.004916 0.002823 -1.741 0.08249 .
BMI -0.018124 0.013438 -1.349 0.17831
MedHx_CVDyes 0.024877 0.104174 0.239 0.81140
stenose50-70% -0.056165 0.616116 -0.091 0.92742
stenose70-90% 0.029985 0.554347 0.054 0.95689
stenose90-99% -0.066973 0.553139 -0.121 0.90370
stenose100% (Occlusion) -0.730295 0.726805 -1.005 0.31569
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9299 on 350 degrees of freedom
Multiple R-squared: 0.192, Adjusted R-squared: 0.1528
F-statistic: 4.893 on 17 and 350 DF, p-value: 1.81e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.323207
Standard error............: 0.051592
Odds ratio (effect size)..: 1.382
Lower 95% CI..............: 1.249
Upper 95% CI..............: 1.529
T-value...................: 6.264673
P-value...................: 1.09668e-09
R^2.......................: 0.192008
Adjusted r^2..............: 0.152763
Sample size of AE DB......: 2423
Sample size of model......: 368
Missing data %............: 84.81222
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes
469.1433 0.2651 0.2845 -0.2341 -0.1561
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.97853 -0.64237 -0.04494 0.58360 2.41010
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 450.887553 78.695200 5.730 1.90e-08 ***
currentDF[, TRAIT] 0.256934 0.042621 6.028 3.57e-09 ***
Age -0.008089 0.005645 -1.433 0.15261
Gendermale 0.306162 0.097611 3.137 0.00183 **
ORdate_year -0.224472 0.039276 -5.715 2.05e-08 ***
Hypertension.compositeyes -0.142938 0.130384 -1.096 0.27357
DiabetesStatusDiabetes -0.116201 0.111687 -1.040 0.29873
SmokerStatusEx-smoker 0.036425 0.097594 0.373 0.70916
SmokerStatusNever smoked 0.143082 0.141142 1.014 0.31128
Med.Statin.LLDyes -0.167205 0.101227 -1.652 0.09931 .
Med.all.antiplateletyes 0.053742 0.158187 0.340 0.73422
GFR_MDRD -0.001307 0.002394 -0.546 0.58537
BMI -0.009911 0.011498 -0.862 0.38918
MedHx_CVDyes 0.040135 0.091466 0.439 0.66103
stenose50-70% -0.392710 0.574523 -0.684 0.49463
stenose70-90% -0.173669 0.530032 -0.328 0.74333
stenose90-99% -0.172148 0.528067 -0.326 0.74459
stenose100% (Occlusion) -0.930035 0.670103 -1.388 0.16589
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8952 on 430 degrees of freedom
Multiple R-squared: 0.1963, Adjusted R-squared: 0.1645
F-statistic: 6.178 on 17 and 430 DF, p-value: 5.855e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.256934
Standard error............: 0.042621
Odds ratio (effect size)..: 1.293
Lower 95% CI..............: 1.189
Upper 95% CI..............: 1.406
T-value...................: 6.028403
P-value...................: 3.566497e-09
R^2.......................: 0.196293
Adjusted r^2..............: 0.164519
Sample size of AE DB......: 2423
Sample size of model......: 448
Missing data %............: 81.51052
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Med.Statin.LLDyes
377.35088 -0.13938 -0.01222 0.31984 -0.18793 -0.25406
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4252 -0.7168 -0.0178 0.6097 2.3442
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 382.837732 133.005231 2.878 0.00428 **
currentDF[, TRAIT] -0.148079 0.059120 -2.505 0.01277 *
Age -0.018197 0.007506 -2.424 0.01591 *
Gendermale 0.381495 0.132668 2.876 0.00432 **
ORdate_year -0.190096 0.066363 -2.864 0.00446 **
Hypertension.compositeyes -0.163857 0.174140 -0.941 0.34747
DiabetesStatusDiabetes -0.056989 0.150146 -0.380 0.70454
SmokerStatusEx-smoker 0.062815 0.129141 0.486 0.62702
SmokerStatusNever smoked 0.343998 0.194722 1.767 0.07829 .
Med.Statin.LLDyes -0.270007 0.129064 -2.092 0.03726 *
Med.all.antiplateletyes 0.085201 0.235966 0.361 0.71829
GFR_MDRD -0.004142 0.003542 -1.169 0.24312
BMI -0.011549 0.016701 -0.692 0.48974
MedHx_CVDyes 0.045507 0.122897 0.370 0.71142
stenose50-70% -0.349383 1.088293 -0.321 0.74840
stenose70-90% -0.193352 1.034790 -0.187 0.85190
stenose90-99% -0.199793 1.032638 -0.193 0.84671
stenose100% (Occlusion) -0.839284 1.225427 -0.685 0.49393
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.003 on 307 degrees of freedom
Multiple R-squared: 0.09275, Adjusted R-squared: 0.04251
F-statistic: 1.846 on 17 and 307 DF, p-value: 0.02231
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.148079
Standard error............: 0.05912
Odds ratio (effect size)..: 0.862
Lower 95% CI..............: 0.768
Upper 95% CI..............: 0.968
T-value...................: -2.504723
P-value...................: 0.01277281
R^2.......................: 0.09275
Adjusted r^2..............: 0.042511
Sample size of AE DB......: 2423
Sample size of model......: 325
Missing data %............: 86.58688
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Med.Statin.LLDyes
369.441292 -0.125855 -0.009407 0.319861 -0.184087 -0.206542
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5441 -0.6935 -0.0074 0.6107 2.4391
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 365.217756 125.908830 2.901 0.00398 **
currentDF[, TRAIT] -0.129343 0.056270 -2.299 0.02216 *
Age -0.014677 0.007287 -2.014 0.04483 *
Gendermale 0.380569 0.126539 3.008 0.00284 **
ORdate_year -0.181562 0.062824 -2.890 0.00411 **
Hypertension.compositeyes -0.113279 0.165456 -0.685 0.49405
DiabetesStatusDiabetes -0.050993 0.142719 -0.357 0.72110
SmokerStatusEx-smoker 0.029611 0.126046 0.235 0.81441
SmokerStatusNever smoked 0.325595 0.186266 1.748 0.08141 .
Med.Statin.LLDyes -0.217762 0.126329 -1.724 0.08570 .
Med.all.antiplateletyes 0.175223 0.214845 0.816 0.41534
GFR_MDRD -0.004207 0.003391 -1.241 0.21561
BMI -0.009384 0.016360 -0.574 0.56665
MedHx_CVDyes 0.065882 0.116576 0.565 0.57237
stenose50-70% -0.180538 1.066122 -0.169 0.86563
stenose70-90% -0.099681 1.019576 -0.098 0.92218
stenose90-99% -0.140547 1.016673 -0.138 0.89013
stenose100% (Occlusion) -0.503419 1.179534 -0.427 0.66981
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9904 on 325 degrees of freedom
Multiple R-squared: 0.08593, Adjusted R-squared: 0.03812
F-statistic: 1.797 on 17 and 325 DF, p-value: 0.02731
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.129343
Standard error............: 0.05627
Odds ratio (effect size)..: 0.879
Lower 95% CI..............: 0.787
Upper 95% CI..............: 0.981
T-value...................: -2.298604
P-value...................: 0.02216168
R^2.......................: 0.085935
Adjusted r^2..............: 0.038122
Sample size of AE DB......: 2423
Sample size of model......: 343
Missing data %............: 85.844
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes Med.Statin.LLDyes
481.9463 0.3897 0.2204 -0.2405 -0.1661 -0.1353
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6353 -0.6508 -0.0352 0.6057 2.4524
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.810e+02 7.720e+01 6.230 1.02e-09 ***
currentDF[, TRAIT] 3.825e-01 4.102e-02 9.324 < 2e-16 ***
Age -2.655e-03 5.378e-03 -0.494 0.6217
Gendermale 2.295e-01 9.296e-02 2.468 0.0139 *
ORdate_year -2.396e-01 3.853e-02 -6.219 1.09e-09 ***
Hypertension.compositeyes -1.494e-01 1.220e-01 -1.224 0.2214
DiabetesStatusDiabetes -6.109e-02 1.038e-01 -0.589 0.5563
SmokerStatusEx-smoker 1.574e-02 9.221e-02 0.171 0.8645
SmokerStatusNever smoked 1.052e-01 1.373e-01 0.766 0.4440
Med.Statin.LLDyes -1.519e-01 9.556e-02 -1.589 0.1127
Med.all.antiplateletyes 3.047e-02 1.470e-01 0.207 0.8358
GFR_MDRD 4.795e-04 2.297e-03 0.209 0.8347
BMI -1.179e-02 1.097e-02 -1.074 0.2832
MedHx_CVDyes 2.197e-02 8.629e-02 0.255 0.7991
stenose50-70% -4.282e-01 5.707e-01 -0.750 0.4535
stenose70-90% -2.634e-01 5.303e-01 -0.497 0.6197
stenose90-99% -3.147e-01 5.287e-01 -0.595 0.5520
stenose100% (Occlusion) -9.036e-01 6.705e-01 -1.348 0.1784
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8978 on 479 degrees of freedom
Multiple R-squared: 0.2369, Adjusted R-squared: 0.2098
F-statistic: 8.748 on 17 and 479 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.382482
Standard error............: 0.04102
Odds ratio (effect size)..: 1.466
Lower 95% CI..............: 1.353
Upper 95% CI..............: 1.589
T-value...................: 9.324199
P-value...................: 4.12287e-19
R^2.......................: 0.236913
Adjusted r^2..............: 0.20983
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
471.7857 0.3464 0.2211 -0.2354 -0.2092
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5725 -0.6842 -0.0106 0.6285 2.2829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.536e+02 7.884e+01 5.753 1.56e-08 ***
currentDF[, TRAIT] 3.404e-01 4.205e-02 8.095 4.74e-15 ***
Age -4.645e-03 5.483e-03 -0.847 0.3973
Gendermale 2.308e-01 9.504e-02 2.428 0.0155 *
ORdate_year -2.258e-01 3.934e-02 -5.740 1.68e-08 ***
Hypertension.compositeyes -1.792e-01 1.247e-01 -1.437 0.1514
DiabetesStatusDiabetes -7.635e-02 1.061e-01 -0.719 0.4723
SmokerStatusEx-smoker 2.884e-02 9.419e-02 0.306 0.7596
SmokerStatusNever smoked 1.377e-01 1.403e-01 0.982 0.3268
Med.Statin.LLDyes -1.409e-01 9.767e-02 -1.443 0.1498
Med.all.antiplateletyes 2.802e-02 1.505e-01 0.186 0.8524
GFR_MDRD 3.006e-04 2.350e-03 0.128 0.8983
BMI -1.342e-02 1.123e-02 -1.195 0.2326
MedHx_CVDyes 3.006e-02 8.811e-02 0.341 0.7332
stenose50-70% -4.718e-01 5.840e-01 -0.808 0.4196
stenose70-90% -2.817e-01 5.427e-01 -0.519 0.6039
stenose90-99% -3.361e-01 5.411e-01 -0.621 0.5348
stenose100% (Occlusion) -1.029e+00 6.860e-01 -1.500 0.1342
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9188 on 480 degrees of freedom
Multiple R-squared: 0.2063, Adjusted R-squared: 0.1782
F-statistic: 7.34 on 17 and 480 DF, p-value: 3.891e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.340369
Standard error............: 0.042046
Odds ratio (effect size)..: 1.405
Lower 95% CI..............: 1.294
Upper 95% CI..............: 1.526
T-value...................: 8.095211
P-value...................: 4.740966e-15
R^2.......................: 0.206317
Adjusted r^2..............: 0.178208
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
430.2780 0.3651 -0.2149
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3229 -0.6695 0.0116 0.6628 2.7270
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 483.732484 119.272545 4.056 6.17e-05 ***
currentDF[, TRAIT] -0.108540 0.057053 -1.902 0.05794 .
Age -0.012458 0.006953 -1.792 0.07403 .
Gendermale 0.369763 0.122970 3.007 0.00283 **
ORdate_year -0.240763 0.059497 -4.047 6.40e-05 ***
Hypertension.compositeyes -0.021201 0.159718 -0.133 0.89448
DiabetesStatusDiabetes -0.039525 0.135510 -0.292 0.77071
SmokerStatusEx-smoker 0.145721 0.119575 1.219 0.22380
SmokerStatusNever smoked 0.359357 0.181365 1.981 0.04833 *
Med.Statin.LLDyes -0.189488 0.122301 -1.549 0.12220
Med.all.antiplateletyes 0.108570 0.194470 0.558 0.57701
GFR_MDRD -0.001517 0.003074 -0.493 0.62206
BMI -0.018579 0.014625 -1.270 0.20480
MedHx_CVDyes 0.071023 0.113512 0.626 0.53193
stenose50-70% -0.045968 0.668659 -0.069 0.94523
stenose70-90% -0.253970 0.593765 -0.428 0.66911
stenose90-99% -0.194770 0.591953 -0.329 0.74233
stenose100% (Occlusion) -0.605486 0.835638 -0.725 0.46920
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9935 on 349 degrees of freedom
Multiple R-squared: 0.09256, Adjusted R-squared: 0.04836
F-statistic: 2.094 on 17 and 349 DF, p-value: 0.006979
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.10854
Standard error............: 0.057053
Odds ratio (effect size)..: 0.897
Lower 95% CI..............: 0.802
Upper 95% CI..............: 1.003
T-value...................: -1.902428
P-value...................: 0.05793809
R^2.......................: 0.092565
Adjusted r^2..............: 0.048363
Sample size of AE DB......: 2423
Sample size of model......: 367
Missing data %............: 84.85349
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
269.319313 -0.009533 0.294462 -0.134292 -0.179835 0.348119
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2611 -0.6760 0.0113 0.6116 2.6236
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 275.964697 125.935591 2.191 0.02915 *
currentDF[, TRAIT] -0.002156 0.054993 -0.039 0.96876
Age -0.014792 0.007229 -2.046 0.04156 *
Gendermale 0.349140 0.127517 2.738 0.00653 **
ORdate_year -0.137038 0.062850 -2.180 0.02996 *
Hypertension.compositeyes -0.121535 0.166625 -0.729 0.46630
DiabetesStatusDiabetes -0.092884 0.144284 -0.644 0.52020
SmokerStatusEx-smoker 0.053903 0.126442 0.426 0.67017
SmokerStatusNever smoked 0.407871 0.188700 2.161 0.03140 *
Med.Statin.LLDyes -0.199705 0.125586 -1.590 0.11279
Med.all.antiplateletyes 0.285589 0.224909 1.270 0.20508
GFR_MDRD -0.003156 0.003388 -0.931 0.35238
BMI -0.014823 0.016144 -0.918 0.35921
MedHx_CVDyes 0.025319 0.119106 0.213 0.83179
stenose50-70% -0.197069 1.067862 -0.185 0.85370
stenose70-90% -0.069896 1.020871 -0.068 0.94546
stenose90-99% -0.130683 1.019824 -0.128 0.89812
stenose100% (Occlusion) -0.819097 1.273028 -0.643 0.52041
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9942 on 318 degrees of freedom
Multiple R-squared: 0.07925, Adjusted R-squared: 0.03003
F-statistic: 1.61 on 17 and 318 DF, p-value: 0.06002
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.002156
Standard error............: 0.054993
Odds ratio (effect size)..: 0.998
Lower 95% CI..............: 0.896
Upper 95% CI..............: 1.111
T-value...................: -0.039197
P-value...................: 0.9687576
R^2.......................: 0.079251
Adjusted r^2..............: 0.030029
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + SmokerStatus, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes SmokerStatusEx-smoker
164.54415 0.35958 0.25142 -0.08215 -0.20282 0.10913
SmokerStatusNever smoked
0.27908
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2591 -0.6027 -0.0388 0.6311 2.5878
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 146.080149 87.467299 1.670 0.0955 .
currentDF[, TRAIT] 0.359164 0.048534 7.400 6.13e-13 ***
Age -0.006303 0.005520 -1.142 0.2541
Gendermale 0.247108 0.095861 2.578 0.0102 *
ORdate_year -0.072430 0.043645 -1.660 0.0977 .
Hypertension.compositeyes -0.147063 0.126216 -1.165 0.2445
DiabetesStatusDiabetes -0.010037 0.107585 -0.093 0.9257
SmokerStatusEx-smoker 0.149550 0.095041 1.574 0.1163
SmokerStatusNever smoked 0.325111 0.140706 2.311 0.0213 *
Med.Statin.LLDyes -0.154385 0.098659 -1.565 0.1183
Med.all.antiplateletyes 0.070551 0.151685 0.465 0.6421
GFR_MDRD 0.001645 0.002388 0.689 0.4911
BMI -0.014946 0.011350 -1.317 0.1885
MedHx_CVDyes 0.020318 0.089007 0.228 0.8195
stenose50-70% -0.450650 0.589894 -0.764 0.4453
stenose70-90% -0.303103 0.548176 -0.553 0.5806
stenose90-99% -0.372707 0.546588 -0.682 0.4956
stenose100% (Occlusion) -0.896842 0.693084 -1.294 0.1963
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.928 on 480 degrees of freedom
Multiple R-squared: 0.1903, Adjusted R-squared: 0.1617
F-statistic: 6.637 on 17 and 480 DF, p-value: 2.58e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.359164
Standard error............: 0.048534
Odds ratio (effect size)..: 1.432
Lower 95% CI..............: 1.302
Upper 95% CI..............: 1.575
T-value...................: 7.400196
P-value...................: 6.131325e-13
R^2.......................: 0.190333
Adjusted r^2..............: 0.161658
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MCP1_rankattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] GFR_MDRD
1.493e-17 1.000e+00 -2.237e-19 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.527e-16 -2.360e-17 -1.770e-18 1.985e-17 5.427e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.652e-15 4.889e-15 3.380e-01 0.736
currentDF[, TRAIT] 1.000e+00 2.594e-18 3.855e+17 <2e-16 ***
Age 1.184e-19 3.309e-19 3.580e-01 0.721
Gendermale 4.281e-18 5.785e-18 7.400e-01 0.460
ORdate_year -8.204e-19 2.440e-18 -3.360e-01 0.737
Hypertension.compositeyes 3.866e-19 7.573e-18 5.100e-02 0.959
DiabetesStatusDiabetes 3.504e-18 6.434e-18 5.450e-01 0.586
SmokerStatusEx-smoker -2.377e-18 5.690e-18 -4.180e-01 0.676
SmokerStatusNever smoked 2.778e-18 8.465e-18 3.280e-01 0.743
Med.Statin.LLDyes -2.396e-18 5.929e-18 -4.040e-01 0.686
Med.all.antiplateletyes -2.923e-18 9.092e-18 -3.210e-01 0.748
GFR_MDRD -2.043e-19 1.423e-19 -1.435e+00 0.152
BMI 4.747e-19 6.810e-19 6.970e-01 0.486
MedHx_CVDyes 1.503e-18 5.339e-18 2.820e-01 0.778
stenose50-70% -6.687e-18 3.541e-17 -1.890e-01 0.850
stenose70-90% -1.188e-17 3.289e-17 -3.610e-01 0.718
stenose90-99% -1.032e-17 3.279e-17 -3.150e-01 0.753
stenose100% (Occlusion) 2.010e-17 4.165e-17 4.830e-01 0.630
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.567e-17 on 480 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 9.691e+33 on 17 and 480 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MCP1_rank ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 1
Standard error............: 0
Odds ratio (effect size)..: 2.718
Lower 95% CI..............: 2.718
Upper 95% CI..............: 2.718
T-value...................: 3.855073e+17
P-value...................: 0
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
515.6169 0.3328 0.2191 -0.2574
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.80412 -0.65997 -0.00457 0.55429 2.56164
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.843e+02 7.641e+01 6.338 5.76e-10 ***
currentDF[, TRAIT] 3.256e-01 4.194e-02 7.763 5.86e-14 ***
Age -7.143e-03 5.453e-03 -1.310 0.1909
Gendermale 2.425e-01 9.497e-02 2.553 0.0110 *
ORdate_year -2.411e-01 3.813e-02 -6.322 6.33e-10 ***
Hypertension.compositeyes -1.164e-01 1.248e-01 -0.933 0.3514
DiabetesStatusDiabetes -1.092e-01 1.058e-01 -1.032 0.3027
SmokerStatusEx-smoker -5.274e-03 9.435e-02 -0.056 0.9554
SmokerStatusNever smoked 1.313e-01 1.369e-01 0.959 0.3380
Med.Statin.LLDyes -1.391e-01 9.780e-02 -1.422 0.1557
Med.all.antiplateletyes 6.493e-02 1.529e-01 0.425 0.6712
GFR_MDRD 6.714e-05 2.306e-03 0.029 0.9768
BMI -1.295e-02 1.101e-02 -1.176 0.2403
MedHx_CVDyes 2.487e-02 8.835e-02 0.282 0.7784
stenose50-70% -5.187e-01 5.616e-01 -0.924 0.3562
stenose70-90% -3.000e-01 5.183e-01 -0.579 0.5630
stenose90-99% -3.373e-01 5.164e-01 -0.653 0.5139
stenose100% (Occlusion) -1.088e+00 6.555e-01 -1.660 0.0975 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.876 on 441 degrees of freedom
Multiple R-squared: 0.2302, Adjusted R-squared: 0.2005
F-statistic: 7.756 on 17 and 441 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.325554
Standard error............: 0.041939
Odds ratio (effect size)..: 1.385
Lower 95% CI..............: 1.276
Upper 95% CI..............: 1.503
T-value...................: 7.762627
P-value...................: 5.85751e-14
R^2.......................: 0.230177
Adjusted r^2..............: 0.200501
Sample size of AE DB......: 2423
Sample size of model......: 459
Missing data %............: 81.05654
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
237.9560 0.3123 0.2260 -0.1187 -0.2196
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.12191 -0.54598 -0.01048 0.59883 2.89938
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.159e+02 8.494e+01 2.542 0.0114 *
currentDF[, TRAIT] 3.202e-01 4.603e-02 6.957 1.16e-11 ***
Age -4.036e-03 5.545e-03 -0.728 0.4671
Gendermale 2.294e-01 9.619e-02 2.384 0.0175 *
ORdate_year -1.072e-01 4.239e-02 -2.529 0.0118 *
Hypertension.compositeyes -2.207e-01 1.269e-01 -1.739 0.0828 .
DiabetesStatusDiabetes 1.837e-02 1.079e-01 0.170 0.8649
SmokerStatusEx-smoker 1.135e-01 9.498e-02 1.195 0.2328
SmokerStatusNever smoked 2.227e-01 1.406e-01 1.585 0.1137
Med.Statin.LLDyes -1.420e-01 9.904e-02 -1.434 0.1522
Med.all.antiplateletyes 1.265e-01 1.528e-01 0.828 0.4081
GFR_MDRD 3.131e-04 2.368e-03 0.132 0.8949
BMI -2.044e-02 1.139e-02 -1.795 0.0734 .
MedHx_CVDyes 7.841e-03 8.954e-02 0.088 0.9303
stenose50-70% -5.181e-01 5.901e-01 -0.878 0.3805
stenose70-90% -3.690e-01 5.462e-01 -0.676 0.4996
stenose90-99% -3.060e-01 5.443e-01 -0.562 0.5743
stenose100% (Occlusion) -1.248e+00 6.909e-01 -1.806 0.0715 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9243 on 474 degrees of freedom
Multiple R-squared: 0.1866, Adjusted R-squared: 0.1574
F-statistic: 6.397 on 17 and 474 DF, p-value: 1.141e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.32018
Standard error............: 0.046025
Odds ratio (effect size)..: 1.377
Lower 95% CI..............: 1.259
Upper 95% CI..............: 1.507
T-value...................: 6.956614
P-value...................: 1.163346e-11
R^2.......................: 0.186607
Adjusted r^2..............: 0.157435
Sample size of AE DB......: 2423
Sample size of model......: 492
Missing data %............: 79.69459
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
604.4820 0.2929 0.2398 -0.3016 -0.2271
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1964 -0.6651 0.0113 0.6225 2.2889
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.803e+02 8.343e+01 6.956 1.18e-11 ***
currentDF[, TRAIT] 2.846e-01 4.567e-02 6.233 1.02e-09 ***
Age -5.768e-03 5.628e-03 -1.025 0.30592
Gendermale 2.556e-01 9.713e-02 2.632 0.00878 **
ORdate_year -2.890e-01 4.164e-02 -6.940 1.30e-11 ***
Hypertension.compositeyes -1.989e-01 1.288e-01 -1.545 0.12313
DiabetesStatusDiabetes -4.949e-02 1.089e-01 -0.454 0.64974
SmokerStatusEx-smoker -2.714e-03 9.658e-02 -0.028 0.97759
SmokerStatusNever smoked 1.362e-01 1.448e-01 0.941 0.34726
Med.Statin.LLDyes -1.385e-01 1.001e-01 -1.384 0.16690
Med.all.antiplateletyes 1.729e-02 1.545e-01 0.112 0.91095
GFR_MDRD 1.248e-04 2.393e-03 0.052 0.95843
BMI -1.447e-02 1.147e-02 -1.261 0.20803
MedHx_CVDyes 1.115e-02 9.069e-02 0.123 0.90223
stenose50-70% -5.050e-01 5.951e-01 -0.849 0.39651
stenose70-90% -3.440e-01 5.507e-01 -0.625 0.53253
stenose90-99% -3.337e-01 5.488e-01 -0.608 0.54347
stenose100% (Occlusion) -1.148e+00 6.963e-01 -1.649 0.09978 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9319 on 470 degrees of freedom
Multiple R-squared: 0.1734, Adjusted R-squared: 0.1435
F-statistic: 5.798 on 17 and 470 DF, p-value: 4.217e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.28463
Standard error............: 0.045667
Odds ratio (effect size)..: 1.329
Lower 95% CI..............: 1.215
Upper 95% CI..............: 1.454
T-value...................: 6.232677
P-value...................: 1.020771e-09
R^2.......................: 0.173361
Adjusted r^2..............: 0.143461
Sample size of AE DB......: 2423
Sample size of model......: 488
Missing data %............: 79.85968
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
445.7351 0.4238 0.2713 -0.2224 -0.1917
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.14418 -0.59601 -0.02638 0.55922 2.18631
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 415.469196 76.990690 5.396 1.13e-07 ***
currentDF[, TRAIT] 0.422204 0.042479 9.939 < 2e-16 ***
Age -0.005294 0.005530 -0.957 0.33890
Gendermale 0.280567 0.093766 2.992 0.00293 **
ORdate_year -0.206761 0.038418 -5.382 1.22e-07 ***
Hypertension.compositeyes -0.168565 0.124543 -1.353 0.17662
DiabetesStatusDiabetes -0.054183 0.106025 -0.511 0.60958
SmokerStatusEx-smoker -0.037196 0.095115 -0.391 0.69594
SmokerStatusNever smoked 0.009232 0.141886 0.065 0.94815
Med.Statin.LLDyes -0.134302 0.097608 -1.376 0.16956
Med.all.antiplateletyes 0.011772 0.147114 0.080 0.93626
GFR_MDRD -0.001411 0.002340 -0.603 0.54677
BMI -0.016033 0.011120 -1.442 0.15009
MedHx_CVDyes 0.063694 0.087746 0.726 0.46830
stenose50-70% -0.347811 0.554132 -0.628 0.53056
stenose70-90% -0.270492 0.513172 -0.527 0.59840
stenose90-99% -0.184304 0.510928 -0.361 0.71848
stenose100% (Occlusion) -0.741191 0.648898 -1.142 0.25400
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8657 on 428 degrees of freedom
Multiple R-squared: 0.2759, Adjusted R-squared: 0.2472
F-statistic: 9.594 on 17 and 428 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.422204
Standard error............: 0.042479
Odds ratio (effect size)..: 1.525
Lower 95% CI..............: 1.403
Upper 95% CI..............: 1.658
T-value...................: 9.939162
P-value...................: 4.407968e-21
R^2.......................: 0.27592
Adjusted r^2..............: 0.24716
Sample size of AE DB......: 2423
Sample size of model......: 446
Missing data %............: 81.59307
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
513.0539 0.3244 0.2028 -0.2560 -0.2241
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3999 -0.6942 -0.0328 0.6255 2.3917
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.961e+02 8.030e+01 6.178 1.39e-09 ***
currentDF[, TRAIT] 3.182e-01 4.337e-02 7.337 9.40e-13 ***
Age -5.803e-03 5.532e-03 -1.049 0.2947
Gendermale 2.150e-01 9.642e-02 2.230 0.0262 *
ORdate_year -2.470e-01 4.008e-02 -6.164 1.51e-09 ***
Hypertension.compositeyes -1.928e-01 1.261e-01 -1.529 0.1269
DiabetesStatusDiabetes -7.730e-02 1.073e-01 -0.720 0.4717
SmokerStatusEx-smoker 3.090e-02 9.526e-02 0.324 0.7458
SmokerStatusNever smoked 1.508e-01 1.418e-01 1.063 0.2881
Med.Statin.LLDyes -1.261e-01 9.876e-02 -1.276 0.2024
Med.all.antiplateletyes 2.581e-02 1.523e-01 0.170 0.8655
GFR_MDRD 4.791e-04 2.377e-03 0.202 0.8404
BMI -1.345e-02 1.135e-02 -1.185 0.2368
MedHx_CVDyes 2.438e-02 8.908e-02 0.274 0.7844
stenose50-70% -5.016e-01 5.904e-01 -0.850 0.3959
stenose70-90% -3.122e-01 5.487e-01 -0.569 0.5697
stenose90-99% -3.628e-01 5.470e-01 -0.663 0.5075
stenose100% (Occlusion) -1.110e+00 6.936e-01 -1.601 0.1100
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9289 on 480 degrees of freedom
Multiple R-squared: 0.1889, Adjusted R-squared: 0.1602
F-statistic: 6.577 on 17 and 480 DF, p-value: 3.716e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.318209
Standard error............: 0.043371
Odds ratio (effect size)..: 1.375
Lower 95% CI..............: 1.263
Upper 95% CI..............: 1.497
T-value...................: 7.336843
P-value...................: 9.395425e-13
R^2.......................: 0.188917
Adjusted r^2..............: 0.160192
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes BMI
233.20323 0.27498 -0.11604 -0.26376 -0.01879
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.84543 -0.64613 -0.01877 0.63724 2.65495
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.281e+02 9.923e+01 2.298 0.0220 *
currentDF[, TRAIT] 2.592e-01 4.730e-02 5.480 7.46e-08 ***
Age -4.643e-03 5.932e-03 -0.783 0.4343
Gendermale 1.255e-01 1.036e-01 1.211 0.2266
ORdate_year -1.132e-01 4.954e-02 -2.285 0.0228 *
Hypertension.compositeyes -1.854e-01 1.382e-01 -1.341 0.1806
DiabetesStatusDiabetes -4.917e-02 1.160e-01 -0.424 0.6720
SmokerStatusEx-smoker 1.822e-02 1.039e-01 0.175 0.8609
SmokerStatusNever smoked 1.956e-01 1.483e-01 1.319 0.1877
Med.Statin.LLDyes -2.696e-01 1.098e-01 -2.456 0.0145 *
Med.all.antiplateletyes 4.911e-02 1.622e-01 0.303 0.7622
GFR_MDRD 9.712e-04 2.639e-03 0.368 0.7131
BMI -2.066e-02 1.251e-02 -1.651 0.0994 .
MedHx_CVDyes 9.816e-02 9.763e-02 1.005 0.3153
stenose50-70% -5.757e-01 5.987e-01 -0.961 0.3369
stenose70-90% -3.310e-01 5.520e-01 -0.600 0.5491
stenose90-99% -2.886e-01 5.498e-01 -0.525 0.5999
stenose100% (Occlusion) -1.009e+00 6.988e-01 -1.444 0.1495
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9313 on 409 degrees of freedom
Multiple R-squared: 0.1491, Adjusted R-squared: 0.1137
F-statistic: 4.215 on 17 and 409 DF, p-value: 6.462e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.259178
Standard error............: 0.047297
Odds ratio (effect size)..: 1.296
Lower 95% CI..............: 1.181
Upper 95% CI..............: 1.422
T-value...................: 5.47978
P-value...................: 7.459196e-08
R^2.......................: 0.14908
Adjusted r^2..............: 0.113712
Sample size of AE DB......: 2423
Sample size of model......: 427
Missing data %............: 82.37722
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Med.Statin.LLDyes
141.838177 0.443802 -0.006814 0.294597 -0.070732 -0.135797
Med.all.antiplateletyes
0.275088
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.04725 -0.57430 -0.01202 0.62814 2.20609
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 152.088685 80.472825 1.890 0.05937 .
currentDF[, TRAIT] 0.437723 0.043170 10.140 < 2e-16 ***
Age -0.007455 0.005274 -1.414 0.15812
Gendermale 0.296610 0.091505 3.241 0.00127 **
ORdate_year -0.075717 0.040148 -1.886 0.05991 .
Hypertension.compositeyes -0.131226 0.120874 -1.086 0.27818
DiabetesStatusDiabetes -0.023393 0.102830 -0.227 0.82014
SmokerStatusEx-smoker 0.078039 0.090790 0.860 0.39046
SmokerStatusNever smoked 0.185541 0.135001 1.374 0.16997
Med.Statin.LLDyes -0.134239 0.094499 -1.421 0.15611
Med.all.antiplateletyes 0.261626 0.145654 1.796 0.07309 .
GFR_MDRD 0.001238 0.002278 0.543 0.58708
BMI -0.006185 0.010877 -0.569 0.56985
MedHx_CVDyes 0.059671 0.085297 0.700 0.48454
stenose50-70% -0.106702 0.566388 -0.188 0.85065
stenose70-90% -0.103307 0.525362 -0.197 0.84419
stenose90-99% -0.122370 0.523727 -0.234 0.81535
stenose100% (Occlusion) -0.262180 0.667961 -0.393 0.69486
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.889 on 480 degrees of freedom
Multiple R-squared: 0.2571, Adjusted R-squared: 0.2308
F-statistic: 9.771 on 17 and 480 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.437723
Standard error............: 0.04317
Odds ratio (effect size)..: 1.549
Lower 95% CI..............: 1.423
Upper 95% CI..............: 1.686
T-value...................: 10.13951
P-value...................: 5.072202e-22
R^2.......................: 0.257082
Adjusted r^2..............: 0.230771
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes
278.9982 0.3708 0.2965 -0.1393 -0.1490
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.86874 -0.65351 -0.04043 0.53752 3.05362
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.700e+02 8.015e+01 3.369 0.000822 ***
currentDF[, TRAIT] 3.642e-01 4.388e-02 8.298 1.33e-15 ***
Age -6.351e-03 5.390e-03 -1.178 0.239320
Gendermale 3.220e-01 9.462e-02 3.403 0.000728 ***
ORdate_year -1.342e-01 4.000e-02 -3.356 0.000859 ***
Hypertension.compositeyes -1.157e-01 1.253e-01 -0.924 0.356229
DiabetesStatusDiabetes -7.654e-02 1.069e-01 -0.716 0.474509
SmokerStatusEx-smoker -1.696e-03 9.395e-02 -0.018 0.985607
SmokerStatusNever smoked 1.277e-01 1.365e-01 0.936 0.349882
Med.Statin.LLDyes -1.610e-01 9.799e-02 -1.643 0.101044
Med.all.antiplateletyes 1.145e-01 1.542e-01 0.742 0.458204
GFR_MDRD 8.789e-05 2.324e-03 0.038 0.969846
BMI -1.387e-02 1.103e-02 -1.257 0.209372
MedHx_CVDyes 4.530e-02 8.848e-02 0.512 0.608906
stenose50-70% -4.430e-01 5.608e-01 -0.790 0.429997
stenose70-90% -2.836e-01 5.175e-01 -0.548 0.584037
stenose90-99% -3.073e-01 5.156e-01 -0.596 0.551548
stenose100% (Occlusion) -1.014e+00 6.543e-01 -1.550 0.121818
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8744 on 436 degrees of freedom
Multiple R-squared: 0.241, Adjusted R-squared: 0.2114
F-statistic: 8.141 on 17 and 436 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.364156
Standard error............: 0.043883
Odds ratio (effect size)..: 1.439
Lower 95% CI..............: 1.321
Upper 95% CI..............: 1.569
T-value...................: 8.298409
P-value...................: 1.33225e-15
R^2.......................: 0.240951
Adjusted r^2..............: 0.211355
Sample size of AE DB......: 2423
Sample size of model......: 454
Missing data %............: 81.2629
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes
433.8263 0.5797 0.1706 -0.2165 -0.1394
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5141 -0.5106 -0.0590 0.4883 2.5977
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 427.287820 68.049889 6.279 7.65e-10 ***
currentDF[, TRAIT] 0.573425 0.036693 15.628 < 2e-16 ***
Age -0.002660 0.004737 -0.562 0.5747
Gendermale 0.192328 0.082276 2.338 0.0198 *
ORdate_year -0.212989 0.033962 -6.271 8.00e-10 ***
Hypertension.compositeyes -0.152974 0.107846 -1.418 0.1567
DiabetesStatusDiabetes -0.020392 0.091840 -0.222 0.8244
SmokerStatusEx-smoker -0.009957 0.081469 -0.122 0.9028
SmokerStatusNever smoked 0.102581 0.120853 0.849 0.3964
Med.Statin.LLDyes -0.146496 0.084536 -1.733 0.0837 .
Med.all.antiplateletyes 0.007738 0.129892 0.060 0.9525
GFR_MDRD -0.000838 0.002031 -0.413 0.6800
BMI -0.005220 0.009716 -0.537 0.5913
MedHx_CVDyes 0.066458 0.076394 0.870 0.3848
stenose50-70% -0.106846 0.505468 -0.211 0.8327
stenose70-90% -0.044287 0.469387 -0.094 0.9249
stenose90-99% -0.067123 0.467905 -0.143 0.8860
stenose100% (Occlusion) -0.347525 0.594619 -0.584 0.5592
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7942 on 479 degrees of freedom
Multiple R-squared: 0.4029, Adjusted R-squared: 0.3817
F-statistic: 19.01 on 17 and 479 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.573425
Standard error............: 0.036693
Odds ratio (effect size)..: 1.774
Lower 95% CI..............: 1.651
Upper 95% CI..............: 1.907
T-value...................: 15.62752
P-value...................: 8.723821e-45
R^2.......................: 0.402862
Adjusted r^2..............: 0.381669
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale Hypertension.compositeyes Med.Statin.LLDyes Med.all.antiplateletyes
-0.1133 0.6670 0.2354 -0.1616 -0.1266 0.1884
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06932 -0.40155 0.04207 0.43059 2.16413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 95.840446 66.583223 1.439 0.15069
currentDF[, TRAIT] 0.660727 0.035931 18.389 < 2e-16 ***
Age 0.002250 0.004495 0.501 0.61686
Gendermale 0.224179 0.077357 2.898 0.00393 **
ORdate_year -0.047658 0.033223 -1.434 0.15209
Hypertension.compositeyes -0.137379 0.101881 -1.348 0.17816
DiabetesStatusDiabetes -0.032830 0.086710 -0.379 0.70514
SmokerStatusEx-smoker 0.101332 0.076614 1.323 0.18659
SmokerStatusNever smoked 0.114707 0.114022 1.006 0.31492
Med.Statin.LLDyes -0.110854 0.079774 -1.390 0.16530
Med.all.antiplateletyes 0.177777 0.122479 1.451 0.14730
GFR_MDRD 0.001653 0.001921 0.861 0.38994
BMI -0.013696 0.009170 -1.494 0.13596
MedHx_CVDyes -0.027234 0.072019 -0.378 0.70549
stenose50-70% -0.509971 0.476871 -1.069 0.28542
stenose70-90% -0.355522 0.443195 -0.802 0.42285
stenose90-99% -0.445780 0.441889 -1.009 0.31358
stenose100% (Occlusion) -0.697120 0.560445 -1.244 0.21415
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7503 on 480 degrees of freedom
Multiple R-squared: 0.4708, Adjusted R-squared: 0.452
F-statistic: 25.12 on 17 and 480 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.660727
Standard error............: 0.035931
Odds ratio (effect size)..: 1.936
Lower 95% CI..............: 1.805
Upper 95% CI..............: 2.077
T-value...................: 18.38887
P-value...................: 1.477764e-57
R^2.......................: 0.470782
Adjusted r^2..............: 0.452039
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + SmokerStatus + BMI,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes SmokerStatusEx-smoker
657.95447 0.32365 0.21844 -0.32809 -0.25562 0.11063
SmokerStatusNever smoked BMI
0.30379 -0.01841
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3008 -0.5935 -0.0500 0.6200 2.4227
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 642.466588 96.040354 6.690 7.88e-11 ***
currentDF[, TRAIT] 0.327490 0.049225 6.653 9.86e-11 ***
Age -0.009134 0.006011 -1.520 0.1294
Gendermale 0.217328 0.105227 2.065 0.0396 *
ORdate_year -0.319786 0.047942 -6.670 8.87e-11 ***
Hypertension.compositeyes -0.226216 0.138958 -1.628 0.1044
DiabetesStatusDiabetes -0.115174 0.117200 -0.983 0.3264
SmokerStatusEx-smoker 0.157607 0.102508 1.538 0.1250
SmokerStatusNever smoked 0.371415 0.156137 2.379 0.0179 *
Med.Statin.LLDyes -0.088078 0.107177 -0.822 0.4117
Med.all.antiplateletyes 0.118234 0.155705 0.759 0.4481
GFR_MDRD -0.001184 0.002523 -0.470 0.6389
BMI -0.021248 0.012637 -1.681 0.0935 .
MedHx_CVDyes 0.126684 0.097007 1.306 0.1924
stenose50-70% -0.356141 0.706324 -0.504 0.6144
stenose70-90% -0.487268 0.656480 -0.742 0.4584
stenose90-99% -0.555999 0.655133 -0.849 0.3966
stenose100% (Occlusion) -1.316272 0.776273 -1.696 0.0908 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9092 on 386 degrees of freedom
Multiple R-squared: 0.1997, Adjusted R-squared: 0.1645
F-statistic: 5.667 on 17 and 386 DF, p-value: 1.641e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.32749
Standard error............: 0.049225
Odds ratio (effect size)..: 1.387
Lower 95% CI..............: 1.26
Upper 95% CI..............: 1.528
T-value...................: 6.652972
P-value...................: 9.858796e-11
R^2.......................: 0.199723
Adjusted r^2..............: 0.164478
Sample size of AE DB......: 2423
Sample size of model......: 404
Missing data %............: 83.32645
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
394.9850 0.1173 0.3072 -0.1971 -0.1996
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4029 -0.6503 -0.0023 0.6538 2.5270
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.727e+02 8.640e+01 4.314 1.97e-05 ***
currentDF[, TRAIT] 1.151e-01 4.569e-02 2.518 0.01213 *
Age -7.562e-03 5.876e-03 -1.287 0.19879
Gendermale 3.330e-01 1.023e-01 3.255 0.00122 **
ORdate_year -1.855e-01 4.312e-02 -4.301 2.08e-05 ***
Hypertension.compositeyes -2.047e-01 1.333e-01 -1.536 0.12529
DiabetesStatusDiabetes -7.342e-02 1.152e-01 -0.638 0.52405
SmokerStatusEx-smoker 9.055e-02 1.010e-01 0.897 0.37036
SmokerStatusNever smoked 3.343e-01 1.514e-01 2.209 0.02767 *
Med.Statin.LLDyes -1.448e-01 1.057e-01 -1.370 0.17140
Med.all.antiplateletyes 1.293e-01 1.600e-01 0.808 0.41953
GFR_MDRD 6.369e-05 2.538e-03 0.025 0.97999
BMI -1.626e-02 1.211e-02 -1.343 0.18005
MedHx_CVDyes 7.531e-02 9.475e-02 0.795 0.42712
stenose50-70% -5.266e-01 6.146e-01 -0.857 0.39202
stenose70-90% -2.725e-01 5.712e-01 -0.477 0.63351
stenose90-99% -2.543e-01 5.695e-01 -0.446 0.65547
stenose100% (Occlusion) -1.070e+00 7.220e-01 -1.481 0.13916
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9661 on 456 degrees of freedom
Multiple R-squared: 0.1171, Adjusted R-squared: 0.08417
F-statistic: 3.557 on 17 and 456 DF, p-value: 2.502e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.115068
Standard error............: 0.045691
Odds ratio (effect size)..: 1.122
Lower 95% CI..............: 1.026
Upper 95% CI..............: 1.227
T-value...................: 2.518373
P-value...................: 0.0121311
R^2.......................: 0.117082
Adjusted r^2..............: 0.084167
Sample size of AE DB......: 2423
Sample size of model......: 474
Missing data %............: 80.43747
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
304.4297 0.3750 0.3703 -0.1520
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4837 -0.5718 -0.0139 0.6128 2.4435
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.849e+02 8.571e+01 3.324 0.000960 ***
currentDF[, TRAIT] 3.568e-01 4.678e-02 7.627 1.39e-13 ***
Age -7.665e-03 5.601e-03 -1.369 0.171785
Gendermale 3.938e-01 9.732e-02 4.046 6.11e-05 ***
ORdate_year -1.419e-01 4.277e-02 -3.318 0.000979 ***
Hypertension.compositeyes -9.213e-02 1.296e-01 -0.711 0.477414
DiabetesStatusDiabetes -1.266e-02 1.097e-01 -0.115 0.908206
SmokerStatusEx-smoker 9.316e-03 9.652e-02 0.097 0.923151
SmokerStatusNever smoked 1.612e-01 1.431e-01 1.126 0.260718
Med.Statin.LLDyes -1.025e-01 9.881e-02 -1.038 0.299983
Med.all.antiplateletyes 1.319e-01 1.559e-01 0.846 0.398008
GFR_MDRD -6.253e-04 2.447e-03 -0.256 0.798406
BMI -1.121e-02 1.171e-02 -0.958 0.338746
MedHx_CVDyes 2.992e-02 9.069e-02 0.330 0.741621
stenose50-70% -2.844e-03 5.902e-01 -0.005 0.996157
stenose70-90% 9.290e-02 5.475e-01 0.170 0.865339
stenose90-99% 1.044e-01 5.459e-01 0.191 0.848380
stenose100% (Occlusion) -6.307e-01 6.909e-01 -0.913 0.361779
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9222 on 459 degrees of freedom
Multiple R-squared: 0.1955, Adjusted R-squared: 0.1657
F-statistic: 6.56 on 17 and 459 DF, p-value: 4.844e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.3568
Standard error............: 0.046779
Odds ratio (effect size)..: 1.429
Lower 95% CI..............: 1.304
Upper 95% CI..............: 1.566
T-value...................: 7.627405
P-value...................: 1.392688e-13
R^2.......................: 0.195463
Adjusted r^2..............: 0.165666
Sample size of AE DB......: 2423
Sample size of model......: 477
Missing data %............: 80.31366
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + SmokerStatus +
Med.all.antiplatelet + BMI + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Hypertension.compositeyes
407.49841 0.47258 -0.01079 0.14007 -0.20233 -0.30045
SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes BMI stenose50-70% stenose70-90%
0.17313 0.32439 0.24655 -0.03177 -0.80804 -0.68502
stenose90-99% stenose100% (Occlusion)
-0.60890 -1.62204
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06911 -0.54053 0.02395 0.54548 2.68890
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 396.174815 79.386931 4.990 8.57e-07 ***
currentDF[, TRAIT] 0.473215 0.041069 11.522 < 2e-16 ***
Age -0.010761 0.005218 -2.062 0.03974 *
Gendermale 0.131128 0.092224 1.422 0.15575
ORdate_year -0.196685 0.039619 -4.964 9.73e-07 ***
Hypertension.compositeyes -0.278728 0.119263 -2.337 0.01986 *
DiabetesStatusDiabetes -0.024611 0.102482 -0.240 0.81032
SmokerStatusEx-smoker 0.177017 0.090168 1.963 0.05023 .
SmokerStatusNever smoked 0.323357 0.132778 2.435 0.01526 *
Med.Statin.LLDyes -0.090403 0.092376 -0.979 0.32827
Med.all.antiplateletyes 0.248239 0.145528 1.706 0.08873 .
GFR_MDRD 0.001309 0.002298 0.570 0.56911
BMI -0.031639 0.011018 -2.871 0.00428 **
MedHx_CVDyes 0.023068 0.084768 0.272 0.78565
stenose50-70% -0.861245 0.549700 -1.567 0.11786
stenose70-90% -0.721187 0.512264 -1.408 0.15985
stenose90-99% -0.651536 0.509866 -1.278 0.20195
stenose100% (Occlusion) -1.677012 0.647752 -2.589 0.00993 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8621 on 459 degrees of freedom
Multiple R-squared: 0.2969, Adjusted R-squared: 0.2708
F-statistic: 11.4 on 17 and 459 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.473215
Standard error............: 0.041069
Odds ratio (effect size)..: 1.605
Lower 95% CI..............: 1.481
Upper 95% CI..............: 1.74
T-value...................: 11.52248
P-value...................: 3.710762e-27
R^2.......................: 0.296872
Adjusted r^2..............: 0.27083
Sample size of AE DB......: 2423
Sample size of model......: 477
Missing data %............: 80.31366
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + SmokerStatus +
Med.all.antiplatelet + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Hypertension.compositeyes
261.74610 0.60020 -0.01143 0.21359 -0.13001 -0.18921
SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes BMI
0.13122 0.23333 0.32574 -0.03044
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.07159 -0.47744 -0.02571 0.49342 2.79265
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 255.302470 72.052050 3.543 0.000436 ***
currentDF[, TRAIT] 0.602903 0.037128 16.238 < 2e-16 ***
Age -0.011154 0.004713 -2.366 0.018380 *
Gendermale 0.202433 0.082269 2.461 0.014238 *
ORdate_year -0.126662 0.035959 -3.522 0.000471 ***
Hypertension.compositeyes -0.169317 0.108823 -1.556 0.120423
DiabetesStatusDiabetes 0.007354 0.092609 0.079 0.936743
SmokerStatusEx-smoker 0.139631 0.081284 1.718 0.086506 .
SmokerStatusNever smoked 0.244867 0.120009 2.040 0.041883 *
Med.Statin.LLDyes -0.106001 0.083422 -1.271 0.204497
Med.all.antiplateletyes 0.276223 0.131469 2.101 0.036183 *
GFR_MDRD 0.001761 0.002075 0.849 0.396498
BMI -0.033145 0.009935 -3.336 0.000919 ***
MedHx_CVDyes 0.047506 0.076557 0.621 0.535216
stenose50-70% -0.296349 0.495684 -0.598 0.550230
stenose70-90% -0.238666 0.460977 -0.518 0.604889
stenose90-99% -0.260377 0.459383 -0.567 0.571131
stenose100% (Occlusion) -1.037848 0.582274 -1.782 0.075346 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7786 on 458 degrees of freedom
Multiple R-squared: 0.4262, Adjusted R-squared: 0.4049
F-statistic: 20.01 on 17 and 458 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.602903
Standard error............: 0.037128
Odds ratio (effect size)..: 1.827
Lower 95% CI..............: 1.699
Upper 95% CI..............: 1.965
T-value...................: 16.2383
P-value...................: 3.674529e-47
R^2.......................: 0.426227
Adjusted r^2..............: 0.40493
Sample size of AE DB......: 2423
Sample size of model......: 476
Missing data %............: 80.35493 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
MCP1 levels vs. vulnerability index
Here we calculate the plaque instability/vulnerability index and visualize the MCP1 levels in plaque.
# Plaque vulnerability
table(AEDB.CEA$Macrophages.bin)
no/minor moderate/heavy
847 992 table(AEDB.CEA$Fat.bin_10)
<10% >10%
542 1316 table(AEDB.CEA$Collagen.bin)
no/minor moderate/heavy
382 1469 table(AEDB.CEA$SMC.bin)
no/minor moderate/heavy
602 1244 table(AEDB.CEA$IPH.bin)
no yes
746 1108 # SPSS code
#
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
#
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
#
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
#
#
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
#
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
#
# COMPUTE Instability=Macro_instab + Fat10_instab + coll_instab + SMC_instab + IPH_instab.
# EXECUTE.
# Fix plaquephenotypes
attach(AEDB.CEA)
# mac instability
AEDB.CEA[,"MAC_Instability"] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB.CEA$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1
# fat instability
AEDB.CEA[,"FAT10_Instability"] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1
# col instability
AEDB.CEA[,"COL_Instability"] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == -999] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB.CEA$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0
# smc instability
AEDB.CEA[,"SMC_Instability"] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == -999] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB.CEA$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0
# iph instability
AEDB.CEA[,"IPH_Instability"] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == -999] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == "no"] <- 0
AEDB.CEA$IPH_Instability[IPH.bin == "yes"] <- 1
detach(AEDB.CEA)
table(AEDB.CEA$MAC_Instability, useNA = "ifany")
0 1 <NA>
847 992 584 table(AEDB.CEA$FAT10_Instability, useNA = "ifany")
0 1 <NA>
542 1316 565 table(AEDB.CEA$COL_Instability, useNA = "ifany")
0 1 <NA>
1469 382 572 table(AEDB.CEA$SMC_Instability, useNA = "ifany")
0 1 <NA>
1244 602 577 table(AEDB.CEA$IPH_Instability, useNA = "ifany")
0 1 <NA>
746 1108 569 # creating vulnerability index
AEDB.CEA <- AEDB.CEA %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
)
table(AEDB.CEA$Plaque_Vulnerability_Index, useNA = "ifany")
0 1 2 3 4 5
713 348 479 535 251 97 # str(AEDB.CEA$Plaque_Vulnerability_Index)
Here we plot the levels of inverse-rank normal transformed MCP1 plaque levels from experiment 1 and 2 to the Plaque vulnerability index.
library(sjlabelled)
attach(AEDB.CEA)The following objects are masked from AEDB.CEA (pos = 3):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_ng_ml_2015,
Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q, AgeGroup, AgeGroupSex, AgeSQR, aid, AlcoholUse, Aldosteron_recode, alg10201, alg10202,
alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3, analgeti, Ang2, angioii, ANGPT2,
anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB, artercon,
Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, BMIGroup, brain401,
brain402, brain403, brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx,
CAD_Dx1, CAD_Dx2, CAD_history, CADPAOD_history, Calc.bin, calcification, CalcificationPlaque, calcium, calcium2, calreg, carbasal, cardioembolic,
Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl, cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog,
CML, COL_Instability, collagen, Collagen.bin, CollagenPlaque, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3, concace2,
concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc, conccalc2,
conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2,
concderm, concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3,
concgluc4, concgrel, concinsu, conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor,
concoth2, concothe, concpros, concpsy5, concren, concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2,
concvit2, concvita, Contralateral_surgery, conwhen, corticos, cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug,
CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61, date_ic_patient, date_ic_researcher, Date.of.birth,
date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2, dateapprox3, dateapprox4, dateend1,
dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4, dateok,
dermacor, DiabetesStatus, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812,
diet813, diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2,
diuretic3, DM, DM.composite, duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, eGFRGroup, EGR, EMMPRIN_45kD,
EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, Eotaxin1_rank, EP_CAD, ep_cad_t_30days,
ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite,
EP_composite_time, EP_coronary, ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days, EP_coronary_time, EP_CVdeath,
ep_cvdeath_t_30days, ep_cvdeath_t_3years, ep_cvdeath_t_90days, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time,
EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years,
EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time,
ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major,
ep_major_t_30days, ep_major_t_3years, ep_major_t_90days, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event,
EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta,
ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time,
EP_strokeCVdeath, ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days,
ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years,
epcoronary.30days, epcoronary.3years, epcoronary.90days, epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days, epdeath.3years,
epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmajor.90days, epmi.30days,
epmi.3years, epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years,
epstroke.90days, epstrokeCVdeath.30days, epstrokeCVdeath.3years, epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol,
everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906, exer9071, exer9072, exer9073, exer9074,
exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40,
FAT10_Instability, Fat10Perc, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis,
FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat,
FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat,
FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis,
FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp,
FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling,
FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat,
FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat,
FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth,
FH_PAV_comp, FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat,
FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth,
FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat,
FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check,
FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med,
Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9, HDL,
HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304,
heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318,
heart319, heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301,
hsCRP_plasma, ht, HYAL55KD, HYALURON, Hypercholesterolemia, Hypertension.composite, Hypertension.drugs, Hypertension.selfreport,
Hypertension.selfreportdrug, Hypertension1, Hypertension2, IL1_Beta, IL10, IL10_rank, IL12, IL12_rank, IL13, IL13_rank, IL17, IL2, IL2_rank, IL21,
IL21_rank, IL4, IL4_rank, IL5, IL5_rank, IL6, IL6_pg_ml_2015, IL6_pg_ug_2015, IL6_rank, IL6R_pg_ml_2015, IL6R_pg_ug_2015, IL8, IL8_pg_ml_2015,
IL8_pg_ug_2015, IL8_rank, IL9, IL9_rank, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG,
INFG_rank, informedconsent, insulin, insuline, INVULDAT, IP10, IP10_rank, IPH, IPH_extended.bin, IPH_Instability, IPH.bin, ironfoli, ironfoli2,
KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final, LDL_finalCU, ldl_source, LDL_var, LDLGroup, leg501, leg502,
leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516, leg517, leg518, leg519, leg520,
LMW1STME, LTB4, LTB4R, MAC_binned, MAC_grouped, MAC_Instability, MAC_SMC_ratio, MAC_SMC_ratio_rank, macmean0, macrophages, Macrophages_LN,
macrophages_location, Macrophages_rank, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1, MCP1_pg_ml_2015, MCP1_pg_ml_2015_rank,
MCP1_pg_ug_2015, MCP1_rank, MCSF_pg_ml_2015, MCSF_pg_ug_2015, MDC, MDC_rank, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal,
Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3, Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh,
Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate,
Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media, MG_H1, MI_Dx, MI_Dx1, MI_Dx2, MIF,
MIF_rank, MIG, MIG_rank, MIP1a, MIP1a_rank, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2_rank, MMP2TIMP2,
MMP8, MMP8_rank, MMP9, MMP9_rank, MMP9TIMP1, MPO_plasma, MRP_14, MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma,
negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL, NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local,
NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma, Number_Events_Sorter,
Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPG_rank, OPN, OPN_2013, OPN_plasma, OR_blood,
Oral.glucose.inh, oralgluc, oralgluc2, oralgluc3, oralgluc4, ORdate_epoch, ORdate_year, ORyear, othanthyp, othcoron, other, other2,
OverallPlaquePhenotype, PAI1_pg_ml_2015, PAI1_pg_ug_2015, PAOD, PARC, PARC_rank, patch, PCSK9_plasma, PDGF_BB_plasma, Percentage_CD14,
Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, Plaque_Vulnerability_Index, plaquephenotype, positibl,
PrimaryLast, PrimaryLast1, prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307,
qual0308, qual0309, qual0310, qual0401, qual0402, qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902,
qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909, qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES,
RANTES_pg_ml_2015, RANTES_pg_ug_2015, RANTES_plasma, RANTES_rank, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos, restenosisOK, rheuma,
rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613, risk614,
risk615, risk616, risk617, risk618, risk619, risk620, SBPGroup, Segment_isolated_Tris_2015, SHBG, sICAM1, sICAM1_rank, SMAD1_5_8, SMAD2, SMAD3,
smc, SMC_binned, SMC_grouped, SMC_Instability, SMC_LN, smc_location, smc_macrophages_ratio, SMC_rank, SMC.bin, smcmean0, SmokerCurrent,
SmokerStatus, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin,
Stenosis_contralateral, Stenosis_ipsilateral, StenoticGroup, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx,
StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt, Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g,
Symptoms.5G, systolic, T_NUMBER, TARC, TARC_rank, TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var,
Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, TGFB_rank, thrombos, thrombus,
thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR,
TimeOR_latest, TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, TNFA_rank, totalchol, totalcholesterol_source,
tractdig, tractdig2, tractdig3, tractdig4, tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Tris_protein_conc_ug_ml_2015, Trop1,
Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque, UPID,
validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001, VEGFA, VEGFA_plasma, VEGFA_rank,
vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, VesselDensity_LN, VesselDensity_rank, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery,
Whichoperation, writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta,
yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1,
yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur,
yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2,
yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe, yearpros, yearpsy5,
yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smokingAEDB.CEA$yeartemp <- as.numeric(year(AEDB.CEA$dateok))
AEDB.CEA[,"ORyearGroup"] <- NA
AEDB.CEA$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AEDB.CEA$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AEDB.CEA)
table(AEDB.CEA$ORyearGroup, AEDB.CEA$ORdate_year)
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
< 2007 81 157 190 185 183 152 0 0 0 0 0 0 0 0 0 0 0 0
> 2007 0 0 0 0 0 0 138 182 159 164 176 149 163 76 85 65 66 52MCP1 plaque levels vs. plaque vulnerability index
- Could you please also provide the figure showing MCP1 levels by symptomatic/asymptomatic plaque, separated in men and women and with the same color code like the other ones (e.g. like the one by age)?
- Similarly, I would like to see a figure with the MCP1 levels (again by sex) across the values of the vulnerability index score (0,1,2,3,4,5). By the way this analysis came up very nicely!!
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ Plaque_Vulnerability_Index, data = AEDB.CEA, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ml_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")p2 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ml_2015_rank",
xlab = "Plaque vulnerability index by gender",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_rank ~ Plaque_Vulnerability_Index, data = AEDB.CEA, method = "kruskal.test")p3 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_rank ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")p4 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p4, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Plaque_Vulnerability_Index, data = AEDB.CEA, method = "kruskal.test")p5 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ml_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_pg_ml_2015_rank ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")p6 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ml_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_rank ~ Plaque_Vulnerability_Index, data = AEDB.CEA, method = "kruskal.test")p7 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p7, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(MCP1_rank ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")p8 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
color = "Gender",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p8, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of the plaque vulnerability indez as a function of plaque MCP1 levels.
TRAITS.PROTEIN.RANK.extra = c("MCP1_pg_ml_2015_rank", "MCP1_rank")
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1, ORdate_epoch) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
# table(currentDF$ORdate_year)
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of MCP1_pg_ml_2015_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_year, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.41240 0.0532549 7.744
Age 0.01012 0.0064653 1.565
Gendermale 0.64970 0.1152482 5.637
ORdate_year -0.19973 0.0002414 -827.253
Intercepts:
Value Std. Error t value
0|1 -402.2399 0.0041 -99035.6337
1|2 -400.8099 0.0927 -4325.4751
2|3 -399.5923 0.1060 -3770.4678
3|4 -398.0243 0.1241 -3208.1030
4|5 -396.2918 0.1820 -2177.0530
Residual Deviance: 3747.495
AIC: 3765.495
Value Std. Error t value p value
currentDF[, PROTEIN] 0.41239580 0.053254882 7.743812 9.647956e-15
Age 0.01011714 0.006465267 1.564844 1.176194e-01
Gendermale 0.64969713 0.115248205 5.637373 1.726638e-08
ORdate_year -0.19972707 0.000241434 -827.253368 0.000000e+00
0|1 -402.23986657 0.004061567 -99035.633734 0.000000e+00
1|2 -400.80988917 0.092662628 -4325.475103 0.000000e+00
2|3 -399.59229384 0.105979499 -3770.467849 0.000000e+00
3|4 -398.02428156 0.124068422 -3208.103031 0.000000e+00
4|5 -396.29182977 0.182031319 -2177.053001 0.000000e+00
Analysis of MCP1_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_year, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.57914 0.081983 7.064
Age 0.01674 0.010018 1.671
Gendermale 0.67004 0.173922 3.853
ORdate_year 0.11245 0.000364 308.898
Intercepts:
Value Std. Error t value
0|1 223.5627 0.0048 46996.7054
1|2 225.2892 0.1979 1138.6231
2|3 226.6480 0.2156 1051.2929
3|4 228.3111 0.2372 962.6942
4|5 229.9356 0.2885 796.9079
Residual Deviance: 1682.273
AIC: 1700.273
Value Std. Error t value p value
currentDF[, PROTEIN] 0.57914246 0.0819826492 7.064208 1.615350e-12
Age 0.01674476 0.0100184222 1.671396 9.464339e-02
Gendermale 0.67004219 0.1739220743 3.852543 1.168977e-04
ORdate_year 0.11245196 0.0003640419 308.898413 0.000000e+00
0|1 223.56270905 0.0047569868 46996.705379 0.000000e+00
1|2 225.28918133 0.1978610688 1138.623089 0.000000e+00
2|3 226.64797644 0.2155897515 1051.292906 0.000000e+00
3|4 228.31109569 0.2371584838 962.694195 0.000000e+00
4|5 229.93564186 0.2885347906 796.907858 0.000000e+00Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of MCP1_pg_ml_2015_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.418370 0.0579871 7.2149
Age 0.005896 0.0120649 0.4887
Gendermale 0.662747 0.1286571 5.1513
ORdate_year -0.211763 0.0008317 -254.6074
Hypertension.compositeyes -0.131494 0.1741966 -0.7549
DiabetesStatusDiabetes -0.136239 0.1374964 -0.9909
SmokerStatusEx-smoker 0.084901 0.1295999 0.6551
SmokerStatusNever smoked 0.552791 0.1842708 2.9999
Med.Statin.LLDyes 0.100217 0.1448559 0.6918
Med.all.antiplateletyes -0.062565 0.2065019 -0.3030
GFR_MDRD -0.002856 0.0040793 -0.7002
BMI -0.003863 0.0236201 -0.1635
MedHx_CVDyes 0.161993 0.1179045 1.3739
stenose50-70% -0.667972 0.1575696 -4.2392
stenose70-90% -0.645306 0.0951343 -6.7831
stenose90-99% -0.796388 0.0979339 -8.1319
stenose100% (Occlusion) -1.570633 0.0108586 -144.6441
stenose50-99% -1.263311 0.0065156 -193.8893
stenose70-99% -1.199791 0.0071226 -168.4488
Intercepts:
Value Std. Error t value
0|1 -427.6886 0.0602 -7103.1843
1|2 -426.1533 0.1353 -3148.7832
2|3 -424.9137 0.1520 -2795.1793
3|4 -423.3275 0.1490 -2840.3173
4|5 -421.6368 0.1983 -2126.3969
Residual Deviance: 3217.477
AIC: 3265.477
Value Std. Error t value p value
currentDF[, PROTEIN] 4.183699e-01 0.0579870631 7.2148842 5.397987e-13
Age 5.896199e-03 0.0120649241 0.4887059 6.250499e-01
Gendermale 6.627466e-01 0.1286570869 5.1512641 2.587365e-07
ORdate_year -2.117629e-01 0.0008317231 -254.6074325 0.000000e+00
Hypertension.compositeyes -1.314941e-01 0.1741966173 -0.7548602 4.503328e-01
DiabetesStatusDiabetes -1.362392e-01 0.1374964136 -0.9908561 3.217558e-01
SmokerStatusEx-smoker 8.490072e-02 0.1295998539 0.6550989 5.124041e-01
SmokerStatusNever smoked 5.527907e-01 0.1842707752 2.9998828 2.700835e-03
Med.Statin.LLDyes 1.002173e-01 0.1448559028 0.6918414 4.890369e-01
Med.all.antiplateletyes -6.256453e-02 0.2065019313 -0.3029731 7.619103e-01
GFR_MDRD -2.856430e-03 0.0040792856 -0.7002280 4.837850e-01
BMI -3.862761e-03 0.0236200979 -0.1635370 8.700956e-01
MedHx_CVDyes 1.619931e-01 0.1179045387 1.3739348 1.694619e-01
stenose50-70% -6.679723e-01 0.1575695959 -4.2392205 2.242973e-05
stenose70-90% -6.453059e-01 0.0951342876 -6.7831058 1.176194e-11
stenose90-99% -7.963882e-01 0.0979338596 -8.1318987 4.226178e-16
stenose100% (Occlusion) -1.570633e+00 0.0108586007 -144.6441313 0.000000e+00
stenose50-99% -1.263311e+00 0.0065156313 -193.8892920 0.000000e+00
stenose70-99% -1.199791e+00 0.0071225894 -168.4487660 0.000000e+00
0|1 -4.276886e+02 0.0602108239 -7103.1843330 0.000000e+00
1|2 -4.261533e+02 0.1353390471 -3148.7832306 0.000000e+00
2|3 -4.249137e+02 0.1520165995 -2795.1793305 0.000000e+00
3|4 -4.233275e+02 0.1490423213 -2840.3173304 0.000000e+00
4|5 -4.216368e+02 0.1982869504 -2126.3969333 0.000000e+00
Analysis of MCP1_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.582091 0.0870366 6.6879
Age 0.007899 0.0148627 0.5315
Gendermale 0.752179 0.1888165 3.9836
ORdate_year 0.072517 0.0009023 80.3660
Hypertension.compositeyes 0.297669 0.2500139 1.1906
DiabetesStatusDiabetes -0.225881 0.2116702 -1.0671
SmokerStatusEx-smoker -0.099253 0.1832088 -0.5418
SmokerStatusNever smoked 0.325615 0.2747025 1.1853
Med.Statin.LLDyes 0.171944 0.1984399 0.8665
Med.all.antiplateletyes -0.117527 0.2979384 -0.3945
GFR_MDRD -0.004244 0.0052909 -0.8020
BMI 0.034714 0.0266318 1.3035
MedHx_CVDyes 0.198649 0.1726682 1.1505
stenose50-70% 0.067700 0.0182550 3.7086
stenose70-90% 0.743339 0.0875836 8.4872
stenose90-99% 0.631206 0.0878806 7.1825
stenose100% (Occlusion) 0.952212 0.0303750 31.3486
Intercepts:
Value Std. Error t value
0|1 144.6067 0.0362 3990.9743
1|2 146.3445 0.2214 660.9133
2|3 147.7665 0.2540 581.8450
3|4 149.3776 0.2662 561.0606
4|5 151.0046 0.3050 495.0780
Residual Deviance: 1504.004
AIC: 1548.004
Value Std. Error t value p value
currentDF[, PROTEIN] 0.582091419 0.0870365671 6.6878950 2.264035e-11
Age 0.007898933 0.0148627060 0.5314600 5.951001e-01
Gendermale 0.752178825 0.1888165035 3.9836498 6.786486e-05
ORdate_year 0.072516969 0.0009023344 80.3659565 0.000000e+00
Hypertension.compositeyes 0.297668763 0.2500138827 1.1906089 2.338071e-01
DiabetesStatusDiabetes -0.225880837 0.2116701594 -1.0671360 2.859104e-01
SmokerStatusEx-smoker -0.099253443 0.1832088094 -0.5417504 5.879905e-01
SmokerStatusNever smoked 0.325614749 0.2747025317 1.1853358 2.358847e-01
Med.Statin.LLDyes 0.171943503 0.1984399313 0.8664763 3.862290e-01
Med.all.antiplateletyes -0.117527397 0.2979383769 -0.3944688 6.932349e-01
GFR_MDRD -0.004243556 0.0052909466 -0.8020411 4.225292e-01
BMI 0.034713834 0.0266318281 1.3034717 1.924138e-01
MedHx_CVDyes 0.198649091 0.1726681767 1.1504673 2.499515e-01
stenose50-70% 0.067699597 0.0182549767 3.7085556 2.084449e-04
stenose70-90% 0.743338980 0.0875835961 8.4871941 2.116860e-17
stenose90-99% 0.631205945 0.0878805931 7.1825408 6.842763e-13
stenose100% (Occlusion) 0.952212176 0.0303749713 31.3485786 1.017278e-215
0|1 144.606657777 0.0362334225 3990.9742906 0.000000e+00
1|2 146.344455249 0.2214276176 660.9132899 0.000000e+00
2|3 147.766496279 0.2539619705 581.8449747 0.000000e+00
3|4 149.377587074 0.2662414694 561.0605568 0.000000e+00
4|5 151.004647042 0.3050118314 495.0779986 0.000000e+00Session information
Version: v1.0.17
Last update: 2020-07-14
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to analyse MCP1 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
_S_
_C_
_W_
**Changes log**
* v1.0.17 Added regular, and per gender boxplots for risk factors, _etc_. Changed coloring for consistency.
* v1.0.16 Create a pg/mL-only version. Switched to a new `.RMD`, but kept versioning.
* v1.0.15 Add sex-stratified plots for MCP1 plaque levels by symptoms and plaque vulnerability index.
* v1.0.14 Add analysis on plasma based MCP1 levels measured through OLINK, n ± 700, limited to symptomatic patients only.
* v1.0.13 Splitting RMDs into plaque-focused, and one including plasma levels of MCP1.
* v1.0.12 Add boxplots of MCP1 levels stratified by confounder/variables.
* v1.0.11 Add analysis of pilot data comparing OLINK-platform based MCP1 levels in plasma and plaque.
* v1.0.10 Add analyses for all three `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add (and fixed) ordinal regression. Double checked which measurement to use.
* v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
* v1.0.8 Fixed error in MCP1 plasma analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively.
* v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
* v1.0.6 Only analyses and figures that end up in the final manuscript.
* v1.0.5 Update with 30- and 90-days survival.
* v1.0.4 Updated with Cox-regressions.
* v1.0.3 Included more models.
* v1.0.2 Bugs fixed.
* v1.0.1 Extended with linear and logistic regressions.
* v1.0.0 Inital version.sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin19.4.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/openblas/0.3.10/lib/libopenblasp-r0.3.10.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] GGally_1.5.0 PerformanceAnalytics_2.0.4 xts_0.12-0 zoo_1.8-8 ggcorrplot_0.1.3.999 Hmisc_4.4-0
[7] Formula_1.2-3 lattice_0.20-41 survminer_0.4.6 survival_3.1-12 patchwork_1.0.1.9000 openxlsx_4.1.5
[13] ggpubr_0.3.0 tableone_0.11.1 labelled_2.4.0 sjPlot_2.8.4 sjlabelled_1.1.5 haven_2.3.0
[19] devtools_2.3.0 usethis_1.6.1 MASS_7.3-51.6 DT_0.13 knitr_1.28 forcats_0.5.0
[25] stringr_1.4.0 purrr_0.3.4 tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0 data.table_1.12.8
[31] naniar_0.5.1 tidyr_1.1.0 dplyr_0.8.5 optparse_1.6.6 readr_1.3.1
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.7 plyr_1.8.6 splines_3.6.3 crosstalk_1.1.0.1 TH.data_1.0-10 inline_0.3.15 digest_0.6.25
[9] htmltools_0.4.0 fansi_0.4.1 checkmate_2.0.0 magrittr_1.5 memoise_1.1.0 cluster_2.1.0 remotes_2.1.1 modelr_0.1.8
[17] matrixStats_0.56.0 sandwich_2.5-1 prettyunits_1.1.1 jpeg_0.1-8.1 colorspace_1.4-1 rvest_0.3.5 mitools_2.4 xfun_0.14
[25] callr_3.4.3 crayon_1.3.4 jsonlite_1.6.1 lme4_1.1-23 glue_1.4.1 gtable_0.3.0 emmeans_1.4.7 sjstats_0.18.0
[33] sjmisc_2.8.4 car_3.0-8 pkgbuild_1.0.8 rstan_2.19.3 abind_1.4-5 scales_1.1.1 mvtnorm_1.1-0 DBI_1.1.0
[41] rstatix_0.5.0.999 ggeffects_0.14.3 Rcpp_1.0.4.6 htmlTable_1.13.3 xtable_1.8-4 performance_0.4.6 foreign_0.8-75 km.ci_0.5-2
[49] stats4_3.6.3 StanHeaders_2.19.2 survey_4.0 htmlwidgets_1.5.1 httr_1.4.1 getopt_1.20.3 RColorBrewer_1.1-2 acepack_1.4.1
[57] ellipsis_0.3.1 reshape_0.8.8 farver_2.0.3 pkgconfig_2.0.3 loo_2.2.0 nnet_7.3-14 dbplyr_1.4.3 reshape2_1.4.4
[65] tidyselect_1.1.0 labeling_0.3 rlang_0.4.6 effectsize_0.3.1 munsell_0.5.0 cellranger_1.1.0 cli_2.0.2 generics_0.0.2
[73] broom_0.5.6 evaluate_0.14 yaml_2.2.1 processx_3.4.2 fs_1.4.1 zip_2.0.4 survMisc_0.5.5 visdat_0.5.3
[81] nlme_3.1-148 xml2_1.3.2 compiler_3.6.3 rstudioapi_0.11 png_0.1-7 curl_4.3 e1071_1.7-3 testthat_2.3.2
[89] ggsignif_0.6.0 reprex_0.3.0 statmod_1.4.34 stringi_1.4.6 ps_1.3.3 parameters_0.7.0 desc_1.2.0 Matrix_1.2-18
[97] nloptr_1.2.2.1 KMsurv_0.1-5 ggsci_2.9 vctrs_0.3.0 pillar_1.4.4 lifecycle_0.2.0 estimability_1.3 insight_0.8.4
[105] latticeExtra_0.6-29 R6_2.4.1 gridExtra_2.3 rio_0.5.16 sessioninfo_1.1.1 codetools_0.2-16 boot_1.3-25 assertthat_0.2.1
[113] pkgload_1.0.2 rprojroot_1.3-2 withr_2.2.0 multcomp_1.4-13 mgcv_1.8-31 bayestestR_0.6.0 parallel_3.6.3 hms_0.5.3
[121] quadprog_1.5-8 rpart_4.1-15 grid_3.6.3 class_7.3-17 coda_0.19-3 minqa_1.2.4 rmarkdown_2.1 carData_3.0-4
[129] lubridate_1.7.8 base64enc_0.1-3 Saving environment
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))| © 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io. |
---
title: "Athero-Express Biobank Study -- MCP1 plaque levels (n ± 1,100; [pg/mL])."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans'
date: '`r Sys.Date()`'
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook
ROOT_loc = "/Users/swvanderlaan"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="MCP1_pg_mL"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

# Sample selection
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       dir.create(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       FALSE)
SELECTIONS_loc = paste0(ANALYSIS_loc, "/SELECTIONS")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, include = FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

We used the Luminex-platform to measure atherosclerotic plaque proteins. Historically, this was done in two experiments: 


**Experiment 1: **

This entails an experiment where also 20+ other interleukins, cyto- and chemokines, and metalloproteinases were measured. Part of these were measured using LUMINEX, some of them were measured using FACS, ELISA, and activity assays. These assays were run according to instructions from the producer in a research setting. 

- variable `MCP1`: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/mL]. Luminex platform.


**Experiment 2: **

This entails an experiment where `MCP1` was measured in a clinical diagnostic settings on a clinically validated Luminex-platform. 
- variable `MCP1_pg_ml_2015`: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/mL]. Luminex platform.


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDBraw <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDBraw)
```

## Plaque protein data
Loading Athero-Express plaque protein measurements from 2015.

```{r LoadAE PlaqueProteins}
library(openxlsx)
AEDB_Protein_2015 <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_Proteins/Cytokines_and_chemokines_2015/20200629_MPCF015-0024.xlsx"), sheet = "for_SPSS_R")

names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "SampleID"] <- "STUDY_NUMBER"

head(AEDB_Protein_2015)

```




## Merging protein data
We will merge these measurements to the AEDB (pg/mL measurements of MCP1). We also gathered more information on the experiment.

```{r merge AEDB and Proteins}
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6_pg_ml"] <- "IL6_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6R_pg_ml"] <- "IL6R_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL8_pg_ml"] <- "IL8_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCP1_pg_ml"] <- "MCP1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "RANTES_pg_ml"] <- "RANTES_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "PAI1_pg_ml"] <- "PAI1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCSF_pg_ml"] <- "MCSF_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Adiponectin_ng_ml"] <- "Adiponectin_ng_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Segment_isolated_Tris"] <- "Segment_isolated_Tris_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Tris_protein_conc_ug_ml"] <- "Tris_protein_conc_ug_ml_2015"

temp <- subset(AEDB_Protein_2015, select = c("STUDY_NUMBER", "IL6_pg_ml_2015", "IL6R_pg_ml_2015", "IL8_pg_ml_2015", "MCP1_pg_ml_2015", "RANTES_pg_ml_2015", "PAI1_pg_ml_2015", "MCSF_pg_ml_2015", "Adiponectin_ng_ml_2015", "Segment_isolated_Tris_2015", "Tris_protein_conc_ug_ml_2015"))

AEDB <- merge(AEDBraw, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
              all.x = TRUE)
rm(temp)

temp <- subset(AEDB, select = c("STUDY_NUMBER", "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015", "Segment_isolated_Tris_2015"))
dim(temp)
head(temp)
rm(temp)   
```

#### Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is. 

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/. 
```{r AEDB: describe}
AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 
```


## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt2G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17


```{r FixSymptoms, message=FALSE, warning=FALSE}
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the _diabetes_ status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

table(AEDB$DiabetesStatus)


# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```


We will also fix the _smoking_ status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire. 

- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?



```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the _alcohol_ status variable.

```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)
table(AEDB$Stroke_history)
table(AEDB$Peripheral.interv)
table(AEDB$MedHx_CVD)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```




# Athero-Express Biobank Study

## Baseline characteristics

We are interested in the following variables at baseline.

- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
  - Never smokers
  - Ex-smokers
  - Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
  - Asymptomatic
  - Amaurosis fugax
  - TIA
  - Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments `MCP1`, and `MCP1_pg_ml_2015`)


```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
# table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

# IL6, IL6R, MCP1 measurements
AEDB$IL6 <- as.numeric(AEDB$IL6) # FACS plaque
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015) # LUMINEX plaque
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1 <- as.numeric(AEDB$MCP1) # LUMINEX plaque
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1_pg_ml_2015 <- as.numeric(AEDB$MCP1_pg_ml_2015) # LUMINEX plaque
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma) # LUMINEX

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$MedHx_CVD <- to_factor(AEDB$MedHx_CVD)


AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```

```{r}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6R_pg_ml_2015",
                   "MCP1", "MCP1_pg_ml_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

### All patients
Showing the baseline table of the whole Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB CEA}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients with `MCP1_pg_ml_2015`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ml_2015`.

```{r Baseline AEDB: Visualize subsetCEA}
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ml_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```

### CEA patients with `MCP1_pg_ml_2015` and `MCP1`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ml_2015` _and_ `MCP1`.

```{r Baseline AEDB: Visualize subsetCEA with MCP1}

AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ml_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```


Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

```


## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, and `inverse-rank normal transformation` (standardise). We know that the proteins are not normally distributed and therefore we will standardise them as follows: 

`z = ( x - μ ) / σ`

Where for each sample, `x` equals the value of the variable, `μ` (_mu_) equals the mean of `x`, and `σ` (_sigma_) equals the standard deviation of `x`.


### MCP1 plaque levels: experiment 2

We will explore the plaque levels. As noted above, we will use `MCP1_pg_ml_2015`, this was experiment 2 in 2015 on the LUMINEX-platform and measurements are in pg/mL.

```{r DataExploration: MCP1 plaque Exp2}

summary(AEDB.CEA$MCP1_pg_ml_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ml_2015, AEDB.CEA$AsymptSympt2G, summary))
```

```{r DataExploration: MCP1 plaque Exp2 visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ml_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ml_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ml_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/mL",
                    ggtheme = theme_minimal())

p1 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p3)
```


### MCP1 plaque levels: experiment 1

We will explore the plaque levels. As noted above, we will use `MCP1`, this was experiment 1 on the LUMINEX-platform and measurements are in pg/mL.

```{r DataExploration: MCP1 plaque Exp1}

# summary(AEDB.CEA$MCP1)
# 
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
# 
attach(AEDB.CEA)
AEDB.CEA$MCP1[MCP1 == 0] <- NA
detach(AEDB.CEA)

summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 plaque Exp1 visual}
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())


AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/mL",
                    ggtheme = theme_minimal())

p1 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p3)

```

#### Correlations between MCP1 plaque levels and transformations

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. 

```{r MCP1 scatters: transformations}
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_rank", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "experiment 1",
                        ylab = "experiment 2",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

```





## Preliminary conclusion data exploration

Based on the `inverse-rank normal transformation` we conclude there are no outliers and the data approximates a normal distribution. We will apply `inverse-rank normal transformation` on all proteins and focus the analysis on MCP1 in plaque.

# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]
13. Year of surgery [`ORdate_year`] as we discovered in Van Lammeren _et al._ the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

## Models

We will analyze the data through four different models

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque MCP1 levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

*Continous traits*

We inspect the plaque characteristics, and `inverse-rank normal transformation` continuous phenotypes.

```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed ('unstable') as compared to 'stable'. 

```{r CrossSec: plaques - transformations and visualisations continuous MAC-SMC}

AEDB.CEA$MAC_SMC_ratio <- AEDB.CEA$macmean0 / AEDB.CEA$smcmean0

AEDB.CEA$MAC_SMC_ratio_rank  <- qnorm((rank(AEDB.CEA$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MAC_SMC_ratio)))


cat("Summary of data.\n")
summary(AEDB.CEA$Macrophages_rank)
summary(AEDB.CEA$SMC_rank)
summary(AEDB.CEA$MAC_SMC_ratio_rank)

ggpubr::gghistogram(AEDB.CEA, "MAC_SMC_ratio_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "macrophages/smooth muscle cells ratio",
                    xlab = "inverse-rank normalized", 
                    ggtheme = theme_minimal())
```


*Binary traits*
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages binned
cat("Summary of data.\n")
summary(AEDB.CEA$Macrophages.bin)
contrasts(AEDB.CEA$Macrophages.bin)

AEDB.CEA$MAC_binned <- as.factor(AEDB.CEA$Macrophages.bin)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_binned)) %>%
  group_by(Gender, MAC_binned) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages grouped
cat("Summary of data.\n")
AEDB.CEA$macrophages <- as.factor(AEDB.CEA$macrophages)
summary(AEDB.CEA$macrophages)
contrasts(AEDB.CEA$macrophages)

AEDB.CEA$MAC_grouped <- as.factor(AEDB.CEA$macrophages)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_grouped)) %>%
  group_by(Gender, MAC_grouped) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (grouped)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)

# SMC binned
cat("Summary of data.\n")
summary(AEDB.CEA$SMC.bin)
contrasts(AEDB.CEA$SMC.bin)

AEDB.CEA$SMC_binned <- as.factor(AEDB.CEA$SMC.bin)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_binned)) %>%
  group_by(Gender, SMC_binned) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)

# SMC grouped
cat("Summary of data.\n")
AEDB.CEA$smc <- as.factor(AEDB.CEA$smc)
summary(AEDB.CEA$smc)
contrasts(AEDB.CEA$smc)

AEDB.CEA$SMC_grouped <- as.factor(AEDB.CEA$smc)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_grouped)) %>%
  group_by(Gender, SMC_grouped) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (grouped)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)


# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

#### Correlations between MCP1 plaque levels and surgery year

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).

```{r MCP1 scatters: year of surgery}
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 1",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

p2 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 2, [pg/mL]",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p2 

rm(p1, p2)

```


In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
TRAITS.PROTEIN.RANK = c("MCP1_pg_ml_2015_rank", "MCP1_rank")

TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "MAC_SMC_ratio_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")


# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AEDB.CEA$ORdate_epoch <- as.numeric(AEDB.CEA$dateok)
AEDB.CEA$ORdate_year <- as.numeric(year(AEDB.CEA$dateok))

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")
summary(AEDB.CEA$ORdate_epoch)

cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")
table(AEDB.CEA$ORdate_year)

COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
# COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

```

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the `inverse-rank normalized` data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque MCP1 levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.


```{r CrossSec: symptoms - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: symptoms - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque MCP1 levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r Calculate new FU cut-offs: maximum FU}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times}

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 30-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 90-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```



# Correlations
We correlated plaque levels of the biomarkers.

## MCP1 plaque levels

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ml_2015_rank",
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

```

```{r MCP1 Correlations table}
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

```

```{r MCP1 Correlations alternative visual 1}
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
```


```{r MCP1 Correlations alternative visual 2}
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ml_2015_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

```


# Additional figures

## Age and sex
We want to create per-age-group figures. 

- Box and Whisker plot for MCP-1 plaque levels by sex.
- Box and Whisker plot for MCP-1 plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).


### Inverse-rank transformed data

```{r MCP1 per Sex, ranked}

# ?ggpubr::ggboxplot()

# Global test
compare_means(MCP1_pg_ml_2015_rank ~ Gender,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Gender.pdf"), plot = last_plot())

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

```{r MCP1 per Sex}

# ?ggpubr::ggboxplot()
compare_means(MCP1_pg_ml_2015 ~ Gender,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Gender.pdf"), plot = last_plot())


```

```{r AgeGroups}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
table(AEDB.CEA$AgeGroupSex)

```

Now we can draw some graphs of plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

```{r MCP1 per AgeGroup per Sex, ranked}

# ?ggpubr::ggboxplot()

# Global test
compare_means(MCP1_pg_ml_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Age groups (years)",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "AgeGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ AgeGroup, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter",
                  ggthemne = theme_minimal()) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup_perGender.pdf"), plot = last_plot())
```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

```{r MCP1 per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
compare_means(MCP1_pg_ml_2015 ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Age groups (years)",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.AgeGroup.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ AgeGroup, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.AgeGroup_perGender.pdf"), plot = last_plot())
```


## Hypertension & blood pressure
We want to create figures of MCP1 levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs. 

- Box and Whisker plot for MCP-1 plaque levels by hypertension group (no, yes)
- Box and Whisker plot for MCP-1 plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)


```{r BloodPressure}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 

table(AEDB.CEA$SBPGroup, AEDB.CEA$Gender)

```

Now we can draw some graphs of plaque MCP1 levels per sex and hypertension/blood pressure group.

### Inverse-rank transformed data

```{r MCP1 per BloodPressure per Sex, ranked}
compare_means(MCP1_pg_ml_2015_rank ~ SBPGroup, data = AEDB.CEA %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.selfreport, data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Self-reported hypertension",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.drugs, data = AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Hypertension medication use",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.HypertensionDrugs.pdf"), plot = last_plot())



compare_means(MCP1_pg_ml_2015_rank ~ SBPGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.selfreport, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.drugs, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())



compare_means(MCP1_pg_ml_2015_rank ~ SBPGroup, group.by = "Hypertension.drugs", data = AEDB.CEA %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Self-reported hypertension by medication use",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())


```


### Raw data

Simalarly but now for the raw data as median ± interquartile range.


```{r MCP1 per BloodPressure per Sex}
compare_means(MCP1_pg_ml_2015 ~ SBPGroup, data = AEDB.CEA %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.SBPGroup.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ Hypertension.selfreport, data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Self-reported hypertension",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Hypertension.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ Hypertension.drugs, data = AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Hypertension medication use",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.HypertensionDrugs.pdf"), plot = last_plot())



compare_means(MCP1_pg_ml_2015 ~ SBPGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.SBPGroup_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ Hypertension.selfreport, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Hypertension_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ Hypertension.drugs, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Hypertension.drugs_byGender.pdf"), plot = last_plot())



compare_means(MCP1_pg_ml_2015 ~ SBPGroup, group.by = "Hypertension.drugs", data = AEDB.CEA %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())




```


## Hypercholesterolemia & LDL levels
We want to create figures of MCP1 levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs. 

- Box and Whisker plot for MCP-1 plaque levels by hypercholesterolemia (`risk614`) group (no, yes)
- Box and Whisker plot for MCP-1 plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for MCP-1 plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)

```{r LDLGroups}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 


table(AEDB.CEA$LDLGroup, AEDB.CEA$Gender)

```


```{r Fix Hypercholesterolemia}
require(sjlabelled)
AEDB.CEA$risk614 <- to_factor(AEDB.CEA$risk614)

# Fix plaquephenotypes
attach(AEDB.CEA)
AEDB.CEA[,"Hypercholesterolemia"] <- NA
AEDB.CEA$Hypercholesterolemia[risk614 == "missing value"] <- NA
AEDB.CEA$Hypercholesterolemia[risk614 == -999] <- NA
AEDB.CEA$Hypercholesterolemia[risk614 == "no"] <- "no"
AEDB.CEA$Hypercholesterolemia[risk614 == "yes"] <- "yes"
detach(AEDB.CEA)

table(AEDB.CEA$risk614, AEDB.CEA$Hypercholesterolemia)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

Now we can draw some graphs of plaque MCP1 levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users.

### Inverse-rank transformed data

```{r MCP1 per Hypercholesterolemia per Sex, ranked}
compare_means(MCP1_pg_ml_2015_rank ~ LDLGroup, data = AEDB.CEA %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ LDLGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups_byGender.pdf"), plot = last_plot())



compare_means(MCP1_pg_ml_2015_rank ~ Hypercholesterolemia, data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypercholesterolemia.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Hypercholesterolemia, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())


compare_means(MCP1_pg_ml_2015_rank ~ Med.Statin.LLD, data = AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Med.Statin.LLD.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Med.Statin.LLD, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())




compare_means(MCP1_pg_ml_2015_rank ~ LDLGroup, group.by = "Med.Statin.LLD", data = AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())


```


### Raw data

Simalarly but now for the raw data as median ± interquartile range.

```{r MCP1 per Hypercholesterolemia per Sex}

compare_means(MCP1_pg_ml_2015 ~ LDLGroup, data = AEDB.CEA %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plaque [pg/mL])",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.LDLGroups.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ LDLGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plaque [pg/mL])",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.LDLGroups_byGender.pdf"), plot = last_plot())



compare_means(MCP1_pg_ml_2015 ~ Hypercholesterolemia, data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "MCP1 plaque [pg/mL])",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Hypercholesterolemia.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ Hypercholesterolemia, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "MCP1 plaque [pg/mL])",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Hypercholesterolemia_byGender.pdf"), plot = last_plot())


compare_means(MCP1_pg_ml_2015 ~ Med.Statin.LLD, data = AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "MCP1 plaque [pg/mL])",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Med.Statin.LLD.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ Med.Statin.LLD, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "MCP1 plaque [pg/mL])",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Med.Statin.LLD_byGender.pdf"), plot = last_plot())




compare_means(MCP1_pg_ml_2015 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "MCP1 plaque [pg/mL])",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AEDB.CEA %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "MCP1 plaque [pg/mL])",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())

```



## Kidney function (eGFR)
We want to create figures of MCP1 levels stratified by kidney function. 

- Box and Whisker plot for MCP-1 plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for MCP-1 plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)

```{r EGFR}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))

table(AEDB.CEA$eGFRGroup, AEDB.CEA$Gender)

table(AEDB.CEA$eGFRGroup, AEDB.CEA$KDOQI)

```

Now we can draw some graphs of plaque MCP1 levels per sex and kidney function group.

### Inverse-rank transformed data


```{r MCP1 per EGFR per Sex, ranked}

# Global test
compare_means(MCP1_pg_ml_2015_rank ~ eGFRGroup, data = AEDB.CEA %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ eGFRGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ KDOQI, data = AEDB.CEA %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.KDOQI.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ KDOQI, group.by = "Gender",  data = AEDB.CEA %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.KDOQI_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ eGFRGroup, data = AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR_KDOQI.pdf"), plot = last_plot())

```


### Raw data

Simalarly but now for the raw data as median ± interquartile range.


```{r MCP1 per EGFR per Sex}

# Global test

compare_means(MCP1_pg_ml_2015 ~ eGFRGroup, data = AEDB.CEA %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.EGFR.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ eGFRGroup, group.by = "Gender",  data = AEDB.CEA %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.EGFR_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ KDOQI, data = AEDB.CEA %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.KDOQI.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ KDOQI, group.by = "Gender",   data = AEDB.CEA %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.KDOQI_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ eGFRGroup,  data = AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.EGFR_KDOQI.pdf"), plot = last_plot())

```



## BMI
We want to create figures of MCP1 levels stratified by BMI. 

- Box and Whisker plot for MCP-1 plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for MCP-1 plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)

```{r BMI}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 

# require(labelled)
# AEDB.CEA$BMI_US <- as_factor(AEDB.CEA$BMI_US)
# AEDB.CEA$BMI_WHO <- as_factor(AEDB.CEA$BMI_WHO)
# table(AEDB.CEA$BMI_WHO, AEDB.CEA$BMI_US)

table(AEDB.CEA$BMIGroup, AEDB.CEA$Gender)
table(AEDB.CEA$BMIGroup, AEDB.CEA$BMI_WHO)

```

Now we can draw some graphs of plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per BMI per Sex, ranked}

# Global test
compare_means(MCP1_pg_ml_2015_rank ~ BMIGroup,  data = AEDB.CEA %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ BMIGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ BMIGroup, data = AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byWHO.pdf"), plot = last_plot())

```


### Raw data

Simalarly but now for the raw data as median ± interquartile range.


```{r MCP1 per BMI per Sex}

# Global test
compare_means(MCP1_pg_ml_2015 ~ BMIGroup,  data = AEDB.CEA %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/mL]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.BMI.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ BMIGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.BMI_byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ BMIGroup,  data = AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.BMI_byWHO.pdf"), plot = last_plot())

```


## Diabetes
We want to create figures of MCP1 levels stratified by type 2 diabetes. 

- Box and Whisker plot for MCP-1 plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

```{r MCP1 per Diabetes per Sex, ranked}

# Global test
compare_means(MCP1_pg_ml_2015_rank ~ DiabetesStatus,  data = AEDB.CEA %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Diabetes status",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ DiabetesStatus, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Diabetes status per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes_byGender.pdf"), plot = last_plot())

```


### Raw data

Simalarly but now for the raw data as median ± interquartile range.


```{r MCP1 per Diabetes per Sex}

# Global test
compare_means(MCP1_pg_ml_2015 ~ DiabetesStatus,  data = AEDB.CEA %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Diabetes status",
                  ylab = "MCP1 plaque [pg/mL]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Diabetes.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ DiabetesStatus, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Diabetes status per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Diabetes_byGender.pdf"), plot = last_plot())


```



## Smoking
We want to create figures of MCP1 levels stratified by smoking. 

- Box and Whisker plot for MCP-1 plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

```{r MCP1 per Smoking per Sex, ranked}

# Global test
compare_means(MCP1_pg_ml_2015_rank ~ SmokerStatus,  data = AEDB.CEA %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Smoker status",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ SmokerStatus, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Smoker status per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking_byGender.pdf"), plot = last_plot())

```


### Raw data

Simalarly but now for the raw data as median ± interquartile range.


```{r MCP1 per Smoking per Sex}

# Global test
compare_means(MCP1_pg_ml_2015 ~ SmokerStatus,  data = AEDB.CEA %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Smoker status",
                  ylab = "MCP1 plaque [pg/mL]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Smoking.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ SmokerStatus, group.by ="Gender", data = AEDB.CEA %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Smoker status per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Smoking_byGender.pdf"), plot = last_plot())

```



## Stenosis
We want to create figures of MCP1 levels stratified by stenosis grade. 

- Box and Whisker plot for MCP-1 plaque levels by stenosis grade group (<70, 70-89, 90+)

```{r Stenosis}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))

table(AEDB.CEA$StenoticGroup, AEDB.CEA$Gender)
table(AEDB.CEA$stenose, AEDB.CEA$StenoticGroup)

```

Now we can draw some graphs of plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

```{r MCP1 per Stenosis per Sex, ranked}

# Global test
compare_means(MCP1_pg_ml_2015_rank ~ StenoticGroup,  data = AEDB.CEA %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Stenotic grade",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ StenoticGroup, group.by ="Gender", data = AEDB.CEA %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Stenotic grade per gender",
                  ylab = "MCP1 plaque [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis_byGender.pdf"), plot = last_plot())

```


### Raw data

Simalarly but now for the raw data as median ± interquartile range.

```{r MCP1 per Stenosis per Sex}

# Global test
compare_means(MCP1_pg_ml_2015 ~ StenoticGroup,  data = AEDB.CEA %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Stenotic grade",
                  ylab = "MCP1 plaque [pg/mL]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Stenosis.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015 ~ StenoticGroup, group.by = "Gender", data = AEDB.CEA %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ml_2015", 
                  xlab = "Stenotic grade per gender",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Stenosis_byGender.pdf"), plot = last_plot())

```


## Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels. 

```{r MCP1 correlations, plaque vs. plaque}
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1", 
                  y = "MCP1_pg_ml_2015",
                  xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
                  ylab = "MCP1 plaque [pg/mL] (exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plaque.raw.pdf"), plot = last_plot())

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_rank", 
                  y = "MCP1_pg_ml_2015_rank",
                  xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
                  ylab = "MCP1 plaque [pg/mL] (exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plaque.rank.pdf"), plot = last_plot())


```


## Symptoms
We want to create per-symptom figures. 

```{r SymptomGroups}
library(dplyr)

table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$AsymptSympt2G)

```

### Inverse-rank transformed data

Now we can draw some graphs of plaque MCP1 levels per symptom group.
```{r MCP1 per SymptomGroups, ranked}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ml_2015_rank",
                  title = "MCP1 plaque [pg/mL] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/mL]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AsymptSympt2G.pdf"), plot = last_plot())

rm(p1)

compare_means(MCP1_pg_ml_2015_rank ~ AsymptSympt2G, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ml_2015_rank",
                  title = "MCP1 plaque [pg/mL] levels per symptom by gender", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/mL]\n inverse-rank transformation",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())

rm(p1)

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

```{r MCP1 per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ml_2015",
                  title = "MCP1 plaque [pg/mL] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.AsymptSympt2G.pdf"), plot = last_plot())

rm(p1)

compare_means(MCP1_pg_ml_2015 ~ AsymptSympt2G, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ml_2015",
                  title = "MCP1 plaque [pg/mL] levels per symptom by gender", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.AsymptSympt2G.byGender.pdf"), plot = last_plot())

rm(p1)

```

## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"

models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1

```

Forest plot for experiment 2.

```{r forestplot plaque, experiment 2}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ml_2015_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ml_2015_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ml_2015_rank"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque MCP-1 levels (1 SD increment, exp. #2, n = 1190+)") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.exp2.forest.pdf"), plot = fp)

rm(fp)
```

Forest plot for experiment 1.

```{r forestplot plaque, experiment 1}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque MCP-1 levels (1 SD increment, exp. #1, n = 490+)") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.exp1.forest.pdf"), plot = fp)

rm(fp)
```

## MCP1 vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data
```{r MCP1 vs Cytokines INRT}
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"


proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))


# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AEDB.CEA <-  AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))

  AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
  
  AEDB.CEA <- AEDB.CEA %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AEDB.CEA[,var.temp.rank] <- NULL
  names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r MCP1 vs Cytokines Histograms}
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ml_2015_rank", proteins_of_interest_rank)

proteins_of_interest_mcp1 <- c("MCP1_pg_ml_2015", proteins_of_interest)

for(PROTEIN in proteins_of_interest_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN in proteins_of_interest_rank_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between `MCP1_pg_ug_2015` and 28 other cytokines (including `MCP1` as measured in experiment 1. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r MCP1 vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c(proteins_of_interest_rank_mcp1)
                                    )

# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2, pg/mL)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r MCP1 vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 6,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())

rm(p1)

```

While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.

```{r MCP1 vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/mL)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 0.8
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9)) 

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_exp2_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_exp2_vs_Cytokines.pdf"), plot = last_plot())
rm(p1)

temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp1)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 0.8
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9)) 

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_exp1_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_exp1_vs_Cytokines.pdf"), plot = last_plot())

rm(p1)

```

Another version - problably not good. 
```{r MCP1 vs Cytokines dotchart}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/mL)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY", #fill = "CytokineY",                              # Color by groups
           palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           ylab = expression(log[10]~"("~italic(p)~")-value"),
           ylim = c(0, 9),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 8, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9))

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.dotchart.MCP1_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.dotchart.MCP1_vs_Cytokines.pdf"), plot = last_plot())

rm(temp, p1)

```


## MCP1 vs. cytokines plaque levels `lm()`

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

## MCP1 levels vs. vulnerability index

Here we calculate the plaque instability/vulnerability index and visualize the MCP1 levels in plaque.

```{r Plaque Vulnerability}
# Plaque vulnerability

table(AEDB.CEA$Macrophages.bin)
table(AEDB.CEA$Fat.bin_10)
table(AEDB.CEA$Collagen.bin)
table(AEDB.CEA$SMC.bin)
table(AEDB.CEA$IPH.bin)

# SPSS code

# 
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
# 
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
# 
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
# 
# 
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
# 
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
# 
# COMPUTE Instability=Macro_instab + Fat10_instab +  coll_instab + SMC_instab + IPH_instab.
# EXECUTE.

# Fix plaquephenotypes
attach(AEDB.CEA)
# mac instability
AEDB.CEA[,"MAC_Instability"] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB.CEA$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1

# fat instability
AEDB.CEA[,"FAT10_Instability"] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1

# col instability 
AEDB.CEA[,"COL_Instability"] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == -999] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB.CEA$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0

# smc instability
AEDB.CEA[,"SMC_Instability"] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == -999] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB.CEA$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0

# iph instability
AEDB.CEA[,"IPH_Instability"] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == -999] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == "no"] <- 0
AEDB.CEA$IPH_Instability[IPH.bin == "yes"] <- 1

detach(AEDB.CEA)

table(AEDB.CEA$MAC_Instability, useNA = "ifany")
table(AEDB.CEA$FAT10_Instability, useNA = "ifany")
table(AEDB.CEA$COL_Instability, useNA = "ifany")
table(AEDB.CEA$SMC_Instability, useNA = "ifany")
table(AEDB.CEA$IPH_Instability, useNA = "ifany")

# creating vulnerability index
AEDB.CEA <- AEDB.CEA %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
                                )

table(AEDB.CEA$Plaque_Vulnerability_Index, useNA = "ifany")

# str(AEDB.CEA$Plaque_Vulnerability_Index)

```

Here we plot the levels of inverse-rank normal transformed `MCP1` plaque levels from experiment 1 and 2 to the `Plaque vulnerability index`. 
```{r Fix ORyearGroup}
library(sjlabelled)

attach(AEDB.CEA)
AEDB.CEA$yeartemp <- as.numeric(year(AEDB.CEA$dateok))
AEDB.CEA[,"ORyearGroup"] <- NA
AEDB.CEA$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AEDB.CEA$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AEDB.CEA)

table(AEDB.CEA$ORyearGroup, AEDB.CEA$ORdate_year)
```

### MCP1 plaque levels vs. plaque vulnerability index

1. Could you please also provide the figure showing MCP1 levels by symptomatic/asymptomatic plaque, separated in men and women and with the same color code like the other ones (e.g. like the one by age)? 
2. Similarly, I would like to see a figure with the MCP1 levels (again by sex) across the values of the vulnerability index score (0,1,2,3,4,5).  By the way this analysis came up very nicely!!


```{r MCP1 per PlaqueVulnerabilityIndex}
# Global test
compare_means(MCP1_pg_ml_2015_rank ~ Plaque_Vulnerability_Index,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Plaque vulnerability index by gender",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())


compare_means(MCP1_rank ~ Plaque_Vulnerability_Index,  data = AEDB.CEA, method = "kruskal.test")
p3 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())

compare_means(MCP1_rank ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")
p4 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p4, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Plaque_Vulnerability_Index, data = AEDB.CEA, method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())

compare_means(MCP1_pg_ml_2015_rank ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AEDB.CEA, method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())


compare_means(MCP1_rank ~ Plaque_Vulnerability_Index,  data = AEDB.CEA, method = "kruskal.test")
p7 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p7, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())

compare_means(MCP1_rank ~ Plaque_Vulnerability_Index, group.by = "Gender",   data = AEDB.CEA, method = "kruskal.test")
p8 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
                  color = "Gender",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p8, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())

```



### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability indez as a function of plaque MCP1 levels.
```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL1 RANK, include=TRUE, paged.print=TRUE}
TRAITS.PROTEIN.RANK.extra = c("MCP1_pg_ml_2015_rank",  "MCP1_rank")

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
  PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1, ORdate_epoch) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    # table(currentDF$ORdate_year)
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
              data  =  currentDF, 
              Hess = TRUE)
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }


```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
  PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
              data  =  currentDF,
              Hess = TRUE)
    
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

```





# Session information

------

    Version:      v1.0.17
    Last update:  2020-07-14
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_
    
    _C_
    
    
    _W_
    
    
    **Changes log**
    * v1.0.17 Added regular, and per gender boxplots for risk factors, _etc_. Changed coloring for consistency. 
    * v1.0.16 Create a pg/mL-only version. Switched to a new `.RMD`, but kept versioning.
    * v1.0.15 Add sex-stratified plots for MCP1 plaque levels by symptoms and plaque vulnerability index.
    * v1.0.14 Add analysis on plasma based MCP1 levels measured through OLINK, n ± 700, limited to symptomatic patients only.
    * v1.0.13 Splitting RMDs into plaque-focused, and one including plasma levels of MCP1.
    * v1.0.12 Add boxplots of MCP1 levels stratified by confounder/variables.
    * v1.0.11 Add analysis of pilot data comparing OLINK-platform based MCP1 levels in plasma and plaque.
    * v1.0.10 Add analyses for all three `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add (and fixed) ordinal regression. Double checked which measurement to use. 
    * v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
    * v1.0.8 Fixed error in MCP1 plasma analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
    * v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
    * v1.0.6 Only analyses and figures that end up in the final manuscript.
    * v1.0.5 Update with 30- and 90-days survival.
    * v1.0.4 Updated with Cox-regressions.
    * v1.0.3 Included more models.
    * v1.0.2 Bugs fixed.
    * v1.0.1 Extended with linear and logistic regressions.
    * v1.0.0 Inital version.
    

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
```

------
<sup>&copy; 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

